distributed generation

Food and Fuel: Solutions for the Future
by Andrew Heintzman , Evan Solomon and Eric Schlosser
Published 2 Feb 2009

In the new scheme of things, power companies would become “virtual utilities,” assisting end-users by connecting them with one another and helping them to share their energy surplus profitably and efficiently. Co-ordinating content rather than producing it will become the mantra of power companies in the era of distributed generation. Utility companies, interestingly enough, serve to gain — at least in the short term — from distributed generation, although, until recently, many have fought the development. Because distributed generation is targeted to the very specific energy requirements of the end-user, it is a less costly and more efficient way of providing additional power than relying on a centralized power source.

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The HEW can be built organically and spread as the distributed generation becomes more widely used. The larger hydrogen fuel cells have the additional advantage of producing pure drinking water as a by-product, a not-insignificant consideration in village communities around the world where access to clean water is often a critical concern. Distributed-generation associations need to be established throughout the developing world. Civil-society organizations, co-operatives (where they exist), micro-credit lending institutions, and local governments ought to view distributed-generation energy webs as the core strategy for building sustainable, self-sufficient communities.

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Similarly, the telegraph and, later, the telephone provided forms of communication that were fast enough to accommodate the quickened pace, flow, density, and interactivity made possible when coal gave way to an even more agile hydrocarbon, crude oil. Today, hydrogen and the new fuel-cell, distributed-generation technology are beginning to fuse with the computer and communications revolution to create a wholly new economic era. Before the distributed generation of hydrogen can be fully achieved, however, changes in the existing power grid will have to be made. That’s where the software and communications revolution fits in. Connecting thousands and then millions of fuel cells to main grids will require sophisticated dispatch and control mechanisms to route energy traffic during peak and non-peak periods.

Shorting the Grid: The Hidden Fragility of Our Electric Grid
by Meredith. Angwin
Published 18 Oct 2020

In RTO areas, with their rules, tariffs, auctions, and wide geographical scope, some of those pressures seem to be relieved. The operative word here is “seem.” The pressures of cost and reliability still exist, but in the RTO area, these are always someone else’s problem. CHAPTER 37 DISTRIBUTED GENERATION MANY RENEWABLE ADVOCATES hope that the world will move away from big, central electricity generators, with their large switchyards and high-voltage transmission lines. Instead, they favor distributed generation, sometimes described as DER (distributed energy resources). These resources will be small generation installations, near the consumer, perhaps owned by the consumer, perhaps part of a microgrid.

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dispatch: The action of a control-room operator issuing electronic or verbal instructions to generators, transmission facilities, and other market participants to start up, shut down, raise or lower generation, and so forth. distributed generation: Generation provided by relatively small installations directly connected to distribution facilities or retail-customer facilities. A rooftop solar photovoltaic system is an example of distributed generation. distribution: The delivery of electricity to end users via low-voltage electric power lines (typically less than 69 kV). It can also mean the transfer of electricity from high-voltage transmission lines to lower-voltage lines.

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Three Issues with Renewables 29. Renewables and Batteries 30. Renewable Policies 31. RECs Are Us 32. Vermont, the Twice-Sold State 33. The Purpose of Renewables 34. Renewables and Auctions The RTO and the Customer 35. The Largest Machine on Earth 36. Overinvesting in Renewables 37. Distributed Generation 38. Personal Responsibility 39. Manipulating the Customer 40. Leaving the RTO Areas Is There a Way Forward? 41. Reliable Electricity 42. A High-Quality Electric Grid 43. Fix the Grid and End the Drama 44. A Brief Look at Ontario 45. What We Can Do Acknowledgments Glossary Endnotes Index About the Author TABLE OF FIGURES Figure 1: Electricity use on the New England grid Figure 2: Duck curve on the California grid Figure 3: RTO areas of North America Figure 4: Fuel mix moves to oil during the cold snap Figure 5: Days of oil stored at a power plant as cold snap progresses Figure 6: Comparison of regulated and RTO states Figure 7: Electricity prices in Texas, deregulated and regulated areas Figure 8: Revenue streams for different types of plants Figure 9: Annual value of New England wholesale electricity markets Figure 10: Transmission planning regions Figure 11: New England wholesale electricity costs, showing rise of transmission costs Figure 12: Graphs of actual demand on the ISO-NE system Figure 13: Fuel mix charts for all fuels and for renewables only Figure 14: August 1, 2016 rally in New York State, celebrating the ZEC Figure 15: New England wholesale electricity costs Figure 16: The ISO-NE queue: 11,000 MW wind and 3,000 MW natural gas Figure 17: Consumer and prosumer Figure 18: Births per woman vs GDP per capita Figure 19: Wind and solar curtailment totals by month ANGELIC MIRACLES AND EASY PROBLEMS CHAPTER 1 THE BIG SHORT The Grid and I I JUST FINISHED REREADING a frightening book, The Big Short,1 which describes the financial meltdown of 2008 and the few people who saw it coming.

Smart Grid Standards
by Takuro Sato
Published 17 Nov 2015

Smart Grid Standards 156 Table 4.2 A comparison of available DER technology options Technology options Advantages Disadvantages Applications Microturbine Low capital cost Compact size Low emissions Insufficient thermal output Low efficiency Fuel cell High conversion efficiency Low emissions Expensive High-temperature corrosion and breakdown (SOFC, MCFC) No mature technology Peak shaving CHP Standby power Power quality and reliability Backup power Electric vehicles Photovoltaic Wind turbine Combustion turbine High power density Quiet operation No emissions No moving parts and low maintenance cost No operation noise Mature technology No emissions No variable costs for fuel Mature technology Relatively high efficiency Low operation cost Great reliability Reciprocating engine Low initial installation cost High installation costs Variable energy output Low conversion efficiency Variable energy output Strict site requirements Low conversion efficiency Environmental issues – emission and noise Consumes more fuel than reciprocating engine when idle Lack of power electronic Short start-up times Mature technology Environmental issues – emission and noise High maintenance Low efficiency High reliability Lack of power electronics CHP Distributed generation Distributed generation Peak shaving Distributed generation Peak shaving CHP Peak shaving Backup power Power quality and reliability Peak shaving Backup power Power quality and reliability CHP DER technologies can be deployed either by customers to achieve energy cost savings and higher energy reliability, or by utilities to lower infrastructure investment and to improve asset utilization.

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Rastler, D. (2004) Economic Costs and Benefits of Distributed Energy Resources. EPRI Technical Update, Energy and Environmental Economics Inc., San Francisco, CA. Herman, D. (2003) Installation, Operation, and maintenance Costs for Distributed Generation Technologies. EPRI Technical Report 1007675. (http://www.epri.com/abstracts/Pages/ ProductAbstract.aspx?ProductId=000000000001007675) European Commission (2002) Distributed Generation with High Penetration of Renewable Energy Sources Project, www.dispower.org (accessed 10 December 2012). European Commission. (2002) The EU Frame Programme 5 MICROGRIDS Project, http://microgrids.power.ece.ntua.gr/ (accessed 10 December 2012).

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Future of the Smart Grid 393 [14] Gellings, C. (2011) EPRI, Estimating the Costs and Benefits of the Smart Grid: A Preliminary Estimate of the Investment Requirements and the Resultant Benefits of a Fully Functioning Smart Grid, EPRI. [15] Felder, F. (2011) The equity implications of smart grid, in Smart Grid Integrating Renewable, Distributed Generation and Energy Efficiency, (ed F.P. Sioshansi) Academic Press, pp. 247–275. [16] S.G. Hauser and K. Crandall. Smart grid is a lot more than just “technology”, in Smart Grid Integrating Renewable, Distributed Generation and Energy Efficiency, (eds FP Sioshansi) Academic Press, pp. 109–153 [17] North American Electric Reliability Corporation (2009) Scenario Reliability Assessment. October 2009. [18] Laitner, S. (2007) Assessing the Potential of Information Technology Applications to Enable Economy-Wide Energy-Efficiency Gains, August 17, 2007. [19] Platt, G., Berry, A. and Cornforth, D. (2011) What role for microgrids?

Green Metropolis: Why Living Smaller, Living Closer, and Driving Less Are Thekeys to Sustainability
by David Owen
Published 16 Sep 2009

And if we need to invest in more power plants or increased distribution capacity to meet the needs of a central city, then we should do it, because when growth takes place there we know that energy is being used more efficiently than it could be otherwise.” This is by no means a widely shared belief among environmentalists, who have generally looked upon big utilities with no more affection than they have for big cities. A growing theme in American environmentalism, in fact, has been the encouragement of “distributed generation”—the decentralizing of the production of electricity by supplementing, and in some cases circumventing, the existing power grid through the creation of smaller and more widely dispersed power-generating facilities, including ones that serve individual buildings. A prominent proponent has been Amory Lovins of RMI, who has tantaliz ingly described a future in which automobiles are replaced by hydrogen-powered “hypercars,” and homeowners generate their own electricity with photovoltaic arrays on their roofs and air-conditioner-size hydrogen-powered fuel cells—which generate electricity without combustion—and produce their own hydrogen, with in-home “hydrogen appliances” fed by natural gas.

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Large generating plants are inherently more efficient than small generators; they also do less damage to the environment per unit of output, since “fitting plants with best available control technology can be financially feasible on a large scale, but not on a small scale.” Greene and Hammerschlag are by no means dismissive of distributed generation, but their paper repeatedly demonstrates that no idea can be judged apart from its real-world context. Heating New York City apartments with individual woodstoves rather than with steam from Con Ed’s centralized steam distribution system—which supplies most buildings in Manhattan below Ninety-sixth Street, and is used to co-generate electricity—would not be a gain for the environment.

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The existing American power grid is antiquated, and it is vulnerable to failures like the one that shut down power in the Northeast in 2003, but breaking it apart is unlikely to be the solution, especially if doing so encourages people to live even farther from one another, thereby increasing wastefulness and environmental damage of all kinds. Part of the fascination with distributed generation, and with the dream of hydrogen as an energy panacea, arises from our very worst impulses—the same yearnings for personal independence that lead to expressways, strip malls, and sprawl. (What is a car but distributed transportation?) The desire to produce your own power in your own basement is akin to the desire to drive yourself to work and swim in your own pool and play tennis on your own court: to be liberated from the grid is to be liberated from other people.

The Burning Answer: The Solar Revolution: A Quest for Sustainable Power
by Keith Barnham
Published 7 May 2015

Having explained this analogy, it is important to emphasise that, in addition to its simplicity and elegance, photovoltaic power has an additional advantage over hydropower and most other forms of renewable energy. A solar cell can generate current and voltage at the place the electrical power is needed. The technical name for this is distributed generation. Compare the physics of the solar cell described in this chapter with the description in the last chapter of the way the electric field is transmitted hundreds of kilometres to boil a kettle. Here is a third possible solution to Hawking’s question in the Introduction. Extant alien civilisations, which found E = hf first, may be observing us from afar.

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In the future I hope we will be able to add: CPV on buildings and in deserts, solar fuel from rooftops and on the large scale in oil-rich countries. What we are discussing really is revolutionary. For more than a century we have assumed that electricity is a commodity that is produced centrally in large generators a long way away. Nowadays people can produce their own electrical power at home. I have called this distributed generation up to now. You may also have seen it described as micro-generation. If the solar revolution is to maintain momentum, gas should increasingly be produced from local agricultural silage, farm waste and landfill. Eventually, I believe, solar fuel will be produced on domestic rooftops. The solar revolution will give new meaning to the 1960s’ slogan ‘Power to the People’.

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It would be infinitely better for the fight against global warming, and for cheaper energy prices, if, instead of effectively bribing farmers and local authorities to accept fracking, government funds were used to encourage farmers to send their waste for anaerobic digestion. Call for effective feed-in tariffs for PV and all other micro-generation All countries should install feed-in-tariff (FIT) arrangements for PV and distributed generation from other solar technologies, similar to those which have been so successful in Germany. The guaranteed prices for solar electricity should be set at levels that encourage installations to expand at the exponential rate that Germany achieved for PV in the years 2005-2010. As in Denmark and Germany, the incentives should be set at levels that encourage community and corporate investment in renewable systems.

The Grid: The Fraying Wires Between Americans and Our Energy Future
by Gretchen Bakke
Published 25 Jul 2016

People can still put solar up on their garage roofs in Hawaii, but the utility won’t connect to them, won’t pay for the electricity they generate, and won’t offer any kind of deal to homeowners on power consumed after dark. This cycle hadn’t yet reached crisis level in 2009, when Chu was listing the known woes of power companies. It’s at crisis level now. What we are bearing witness to are the early days of a variable and distributed generation revolution. Electricity is being made everywhere, by power producers of all sorts and sizes, and increasingly from uncontrollable and largely unpredictable means. And because of an awkward piece of legislation called the Energy Policy Act (1992), which laid the foundation for the deregulation of the electricity industry, in many places not only have the utilities lost control of who makes power and how and where they make it, but they have also lost the right to own power plants themselves.

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And all this new small-power generation was scattered to the four winds—in the river canyons and old mines of the Sierra Nevadas, in the desert outside L.A., in the passes between San Francisco Bay and the Central Valley, even in downtown Sacramento, and almost all of it was variable. It blew with the wind, fell with the water, and warmed with the sun. The utilities quickly found themselves with a plethora of new problems. Never before had they had to deal with variable generation, never before had they had to deal with distributed generation, and never in the seventy years of their existence had they lost control over the production side of their business. At issue wasn’t that they suddenly had to integrate a massive amount of new power but that they weren’t getting to decide how much, where, or when relatively small amounts of electricity would come streaming onto their power lines.

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For half a century the daily, quarterly, and annual running of the grid had been accomplished in meetings between the managers of various arms of a utility. Notes were taken, plans made, and later operationalized. In place of this centralized and somewhat perfunctory organizational routine the utilities now needed something like real-time flexibility. Even if the scale was initially modest, the introduction of variable, distributed generation for which they would have to pay a market price was, for the utilities, a little like aliens coming down from outer space and asking them to enter into an intergalactic energy alliance. It’s fine in theory, but unimaginable in its details. Take a simple seeming thing like paying market price.

The Third Industrial Revolution: How Lateral Power Is Transforming Energy, the Economy, and the World
by Jeremy Rifkin
Published 27 Sep 2011

As early as 2001, the Electric Power Research Institute (EPRI) observed in its report, “Perspectives for the Future,” that distributed generation would likely evolve in much the same way the computer industry has evolved. Large mainframe computers have given way to small, geographically dispersed desktop and laptop machines that are interconnected into fully integrated, extremely flexible networks. In our industry, central-station plants will continue to play an important role, of course. But we’re increasingly going to need smaller, cleaner, widely distributed generators . . . all supported by energy storage technologies. A basic requirement for such a system will be advanced electronic controls: these will be absolutely essential for handling the tremendous traffic of information and power that such a complicated interconnection will bring.42 The IBM guys in Germany put me in touch with Guido Bartels, a Dutch national who was doing a lot of work pushing IBM’s intelligent utility network concept around the world.

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In March of 2006, around the same time Kroes was out on the hustings talking up “unbundling,” Utz Claassen, the tough CEO of EnBW, the fourth largest power and utility company in Germany, invited me to Berlin to speak to his company and clients on climate change, energy security, and the transformation of the power and utility sector. Even though 45 percent of EnBW was owned by EDF of France, a company that produces 78 percent of French electricity from nuclear power, Claassen picked up on the theme of distributed generation of renewable energy.46 Three months later, he invited me to Heilbronn, Germany, to address his entire company. Some five hundred employees filled the hall. After I laid out the vision of a Third Industrial Revolution, Claassen took the podium. To the surprise of many of his employees, who had cut their teeth on conventional fossil fuels and nuclear energy and were used to a centralized, top-down flow of power, Claassen said the energy market was changing and so was EnBW.

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Greenpeace comes down in the middle on the debate. Sven Teske, Greenpeace’s international renewable energy director, supports Desertec, but with the qualification that it should be developed alongside local renewable energy generation initiatives across the continent.22 The struggle over centralized versus distributed generation of renewable energy is intensifying around the world. For my part, while I don’t oppose some centralized applications of solar, wind, hydro, geothermal, and biomass power, they are likely to make up a small portion of the renewable energy generated to power a Third Industrial Revolution economy.

The Price Is Wrong: Why Capitalism Won't Save the Planet
by Brett Christophers
Published 12 Mar 2024

Gabor, ‘The Wall Street Consensus’, Development and Change 52: 3 (2021), 429–59. 18 G. Plimmer, ‘Renewables Projects Face 10-Year Wait to Connect to Electricity Grid’, Financial Times, 8 May 2022. 19 M. Keay, J. Rhys and D. Robinson, ‘Electricity Markets and Pricing for the Distributed Generation Era’, in F. Sioshansi, ed., Distributed Generation and Its Implications for the Utility Industry (Oxford: Academic Press, 2014), pp. 172, 175. 20 ‘A World Turned Upside Down’, Economist, 25 February 2017. 21 IEA, ‘Net Zero by 2050: A Roadmap for the Global Energy Sector’, May 2021, iea.org, p. 164. 22 Email to author, 4 October 2021. 23 Y.

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Just as important as these technical delimitations are scalar and commercial ones. This is a book about the development, ownership and operation of market-facing solar- and wind-based power-generating facilities, and the sale of the electricity that is produced. Two key points flow from this. First, the book is not about small-scale ‘distributed’ generation of renewable power for one’s own use, by say community groups or individual households. Such generation has and will continue to play an important role in the energy transition, albeit to varying degrees in different countries. Drivers of the uptake of renewables are very different in that segment of the market; indeed, for the actors concerned, price may well be the key determinant of an investment decision.

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To be sure, if these technologies are going to have anything like the long-term transformative impact on overall planetary health that is increasingly expected of them, then large-scale facilities feeding large amounts of electricity into the grid – and with the capacity to cleanly power large numbers of households, businesses and buildings – are clearly the priority. But, in any sensible and flexible energy future, there is, and must be, a place for so-called ‘distributed generation’, meaning the smaller-scale generation of electricity for use largely or exclusively in situ, at the local level. Household rooftop solar panels are one (perhaps the classic) example; another would be community-owned wind turbines. Needless to say, household or community-owned nuclear reactors are not on the cards.

Advanced Stochastic Models, Risk Assessment, and Portfolio Optimization: The Ideal Risk, Uncertainty, and Performance Measures
by Frank J. Fabozzi
Published 25 Feb 2008

DECOMPOSITION OF TIME SERIES REPRESENTATION OF TIME SERIES WITH DIFFERENCE EQUATIONS APPLICATION: THE PRICE PROCESS CONCEPTS EXPLAINED IN THIS CHAPTER (IN ORDER OF PRESENTATION) PART Two - Basic Probability Theory CHAPTER 8 - Concepts of Probability Theory HISTORICAL DEVELOPMENT OF ALTERNATIVE APPROACHES TO PROBABILITY SET OPERATIONS AND PRELIMINARIES PROBABILITY MEASURE RANDOM VARIABLE CONCEPTS EXPLAINED IN THIS CHAPTER (IN ORDER OF PRESENTATION) CHAPTER 9 - Discrete Probability Distributions DISCRETE LAW BERNOULLI DISTRIBUTION BINOMIAL DISTRIBUTION HYPERGEOMETRIC DISTRIBUTION MULTINOMIAL DISTRIBUTION POISSON DISTRIBUTION DISCRETE UNIFORM DISTRIBUTION CONCEPTS EXPLAINED IN THIS CHAPTER (IN ORDER OF PRESENTATION) CHAPTER 10 - Continuous Probability Distributions CONTINUOUS PROBABILITY DISTRIBUTION DESCRIBED DISTRIBUTION FUNCTION DENSITY FUNCTION CONTINUOUS RANDOM VARIABLE COMPUTING PROBABILITIES FROM THE DENSITY FUNCTION LOCATION PARAMETERS DISPERSION PARAMETERS CONCEPTS EXPLAINED IN THIS CHAPTER (IN ORDER OF PRESENTATION) CHAPTER 11 - Continuous Probability Distributions with Appealing Statistical Properties NORMAL DISTRIBUTION CHI-SQUARE DISTRIBUTION STUDENT’S t-DISTRIBUTION F-DISTRIBUTION EXPONENTIAL DISTRIBUTION RECTANGULAR DISTRIBUTION GAMMA DISTRIBUTION BETA DISTRIBUTION LOG-NORMAL DISTRIBUTION CONCEPTS EXPLAINED IN THIS CHAPTER (IN ORDER OF PRESENTATION) CHAPTER 12 - Continuous Probability Distributions Dealing with Extreme Events GENERALIZED EXTREME VALUE DISTRIBUTION GENERALIZED PARETO DISTRIBUTION NORMAL INVERSE GAUSSIAN DISTRIBUTION α-STABLE DISTRIBUTION CONCEPTS EXPLAINED IN THIS CHAPTER (IN ORDER OF PRESENTATION) CHAPTER 13 - Parameters of Location and Scale of Random Variables PARAMETERS OF LOCATION PARAMETERS OF SCALE CONCEPTS EXPLAINED IN THIS CHAPTER (IN ORDER OF PRESENTATION) CHAPTER 14 - Joint Probability Distributions HIGHER DIMENSIONAL RANDOM VARIABLES JOINT PROBABILITY DISTRIBUTION MARGINAL DISTRIBUTIONS DEPENDENCE COVARIANCE AND CORRELATION SELECTION OF MULTIVARIATE DISTRIBUTIONS CONCEPTS EXPLAINED IN THIS CHAPTER (IN ORDER OF PRESENTATION) CHAPTER 15 - Conditional Probability and Bayes’ Rule CONDITIONAL PROBABILITY INDEPENDENT EVENTS MULTIPLICATIVE RULE OF PROBABILITY BAYES’ RULE CONDITIONAL PARAMETERS CONCEPTS EXPLAINED IN THIS CHAPTER (IN ORDER OF PRESENTATION) CHAPTER 16 - Copula and Dependence Measures COPULA ALTERNATIVE DEPENDENCE MEASURES CONCEPTS EXPLAINED IN THIS CHAPTER (IN ORDER OF PRESENTATION) PART Three - Inductive Statistics CHAPTER 17 - Point Estimators SAMPLE, STATISTIC, AND ESTIMATOR QUALITY CRITERIA OF ESTIMATORS LARGE SAMPLE CRITERIA MAXIMUM LIKEHOOD ESTIMATOR EXPONENTIAL FAMILY AND SUFFICIENCY CONCEPTS EXPLAINED IN THIS CHAPTER (IN ORDER OF PRESENTATION) CHAPTER 18 - Confidence Intervals CONFIDENCE LEVEL AND CONFIDENCE INTERVAL CONFIDENCE INTERVAL FOR THE MEAN OF A NORMAL RANDOM VARIABLE CONFIDENCE INTERVAL FOR THE MEAN OF A NORMAL RANDOM VARIABLE WITH UNKNOWN VARIANCE CONFIDENCE INTERVAL FOR THE VARIANCE OF A NORMAL RANDOM VARIABLE CONFIDENCE INTERVAL FOR THE VARIANCE OF A NORMAL RANDOM VARIABLE WITH UNKNOWN MEAN CONFIDENCE INTERVAL FOR THE PARAMETER P OF A BINOMIAL DISTRIBUTION CONFIDENCE INTERVAL FOR THE PARAMETER λ OF AN EXPONENTIAL DISTRIBUTION CONCEPTS EXPLAINED IN THIS CHAPTER (IN ORDER OF PRESENTATION) CHAPTER 19 - Hypothesis Testing HYPOTHESES ERROR TYPES QUALITY CRITERIA OF A TEST EXAMPLES CONCEPTS EXPLAINED IN THIS CHAPTER (INORDER OF PRESENTATION) PART Four - Multivariate Linear Regression Analysis CHAPTER 20 - Estimates and Diagnostics for Multivariate Linear Regression Analysis THE MULTIVARIATE LINEAR REGRESSION MODEL ASSUMPTIONS OF THE MULTIVARIATE LINEAR REGRESSION MODEL ESTIMATION OF THE MODEL PARAMETERS DESIGNING THE MODEL DIAGNOSTIC CHECK AND MODEL SIGNIFICANCE APPLICATIONS TO FINANCE CONCEPTS EXPLAINED IN THIS CHAPTER (IN ORDER OF PRESENTATION) CHAPTER 21 - Designing and Building a Multivariate Linear Regression Model THE PROBLEM OF MULTICOLLINEARITY INCORPORATING DUMMY VARIABLES AS INDEPENDENT VARIABLES MODEL BUILDING TECHNIQUES CONCEPTS EXPLAINED IN THIS CHAPTER (IN ORDER OF PRESENTATION) CHAPTER 22 - Testing the Assumptions of the Multivariate Linear Regression Model TESTS FOR LINEARITY ASSUMED STATISTICAL PROPERTIES ABOUT THE ERROR TERM TESTS FOR THE RESIDUALS BEING NORMALLY DISTRIBUTED TESTS FOR CONSTANT VARIANCE OF THE ERROR TERM (HOMOSKEDASTICITY) ABSENCE OF AUTOCORRELATION OF THE RESIDUALS CONCEPTS EXPLAINED IN THIS CHAPTER (IN ORDER OF PRESENTATION) APPENDIX A - Important Functions and Their Features APPENDIX B - Fundamentals of Matrix Operations and Concepts APPENDIX C - Binomial and Multinomial Coefficients APPENDIX D - Application of the Log-Normal Distribution to the Pricing of Call Options References Index The Frank J.

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It states that a sum of random variables with identical distributions and being independent of each other, results in a normal random variable.121 We restate this formally as follows: Let X1, X2, …, Xn be identically distributed random variables with mean E ( Xi ) = µ and Var ( Xi ) = σ 2 and do not influence the outcome of each other (i.e., are independent). Then, we have(11.2) as the number n approaches infinity. The D above the convergence arrow in equation (11.2) indicates that the distribution function of the left expression convergences to the standard normal distribution. Generally, for n = 30 in equation (11.2), we consider equality of the distributions; that is, the left-hand side is N(0,1) distributed. In certain cases, depending on the distribution of the Xi and the corresponding parameter values, n < 30 justifies the use of the standard normal distribution for the left-hand side of equation (11.2).

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That brings to light the immense risk inherent in the return distributions when they are truly α-stable. CONCEPTS EXPLAINED IN THIS CHAPTER (IN ORDER OF PRESENTATION) Heavy tails Generalized extreme value distributions Standardized data Extreme value theory Gumbel distribution Fréchet distribution Weibull distribution Generalized Pareto distribution Normal inverse Gaussian distribution Bessel function of the third kind Scaling property α-stable distributions Stable distributions Tail index Characteristic exponent Excess kurtosis Power law Skewness Scale Location Stability property Generalized central limit theorem CHAPTER 13 Parameters of Location and Scale of Random Variables In the previous four chapters, we presented discrete and continuous probability distributions.

The new village green: living light, living local, living large
by Stephen Morris
Published 1 Sep 2007

One way of addressing the inefficiency of power transmission and distribution is through what power companies call “distributed generation.” Distributed generation is where electricity is fed into power lines by small, scattered power plants such as wind mills, solar electric arrays on roof tops, small-scale hydro power, methane captured at farms or landfills, or fossil fuel powered turbines. The energy from these decentralized power sources tends to be used on-site by whoever is producing it, with the excess going out onto the grid for everyone else. Energy security analysts think distributed generation is preferable because reducing the demand on a single, central power plant makes widespread power interruptions less likely.

Monte Carlo Simulation and Finance
by Don L. McLeish
Published 1 Apr 2005

Here we generate a point (Z1 , Z2 )from the uniform distribution on the unit circle by rejection, generating the point initially from the square −1 · z1 · 1, −1 · z2 · 1 and accepting it when it falls in the unit circle or if z12 + z22 · 1. Now suppose that the points (Z1 , Z2 ) is uniformly distributed GENERATING RANDOM NUMBERS FROM NON-UNIFORM CONTINUOUS DISTRIBUTIONS131 inside the unit circle. Then for r > 0, q P [ −2 log(Z12 + Z22 ) · r] = P [Z12 + Z22 ≥ exp(−r2 /2)] 1 − area of a circle of radius exp(−r2 /2) area of a circle of radius 1 = 1 − e−r 2 /2 . This is exactly the same cumulative distribution function as that of the random variable R in Theorem 21.

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Clearly this is often not a very efficient method, particularly in cases in which the chain mixes or forgets its past very slowly for in this case the required initial transient is long. On the other hand if we shortened it, we run the risk of introducing bias into our simulations because the distribution generated is too far from the equilibrium distribution π. There are a number of solutions to this problem proposed in a burgeoning literature. Here we limit ourselves to a few of the simpler methods. 178 CHAPTER 3. BASIC MONTE CARLO METHODS Metropolis-Hastings Algorithm The Metropolis-Hastings Algorithm is a method for generating random variables from a distribution π that applies even in the case of an infinite number of states or a continuous distribution π.

Python for Finance
by Yuxing Yan
Published 24 Apr 2014

Finance The put-call ratio The put-call ratio for a short period with a trend Summary Exercises [ vii ] 256 257 258 259 261 268 268 269 270 271 275 276 277 278 279 280 281 282 283 284 285 286 288 288 289 290 292 293 294 294 295 297 299 300 300 302 303 304 Table of Contents Chapter 11: Monte Carlo Simulation and Options Generating random numbers from a standard normal distribution Drawing random samples from a normal (Gaussian) distribution Generating random numbers with a seed Generating n random numbers from a normal distribution Histogram for a normal distribution 307 308 309 309 310 310 Graphical presentation of a lognormal distribution Generating random numbers from a uniform distribution Using simulation to estimate the pi value Generating random numbers from a Poisson distribution Selecting m stocks randomly from n given stocks Bootstrapping with/without replacements Distribution of annual returns Simulation of stock price movements Graphical presentation of stock prices at options' maturity dates Finding an efficient portfolio and frontier Finding an efficient frontier based on two stocks 311 312 313 315 315 317 319 320 322 324 324 Constructing an efficient frontier with n stocks Geometric versus arithmetic mean Long-term return forecasting Pricing a call using simulation Exotic options Using the Monte Carlo simulation to price average options Pricing barrier options using the Monte Carlo simulation Barrier in-and-out parity Graphical presentation of an up-and-out and up-and-in parity Pricing lookback options with floating strikes Using the Sobol sequence to improve the efficiency Summary Exercises 329 332 333 334 335 335 337 339 340 342 344 344 345 Impact of different correlations Chapter 12: Volatility Measures and GARCH Conventional volatility measure – standard deviation Tests of normality Estimating fat tails Lower partial standard deviation Test of equivalency of volatility over two periods Test of heteroskedasticity, Breusch, and Pagan (1979) Retrieving option data from Yahoo!

No Ordinary Disruption: The Four Global Forces Breaking All the Trends
by Richard Dobbs and James Manyika
Published 12 May 2015

Prices for lithium-ion battery packs for cars could fall from over $500 per MWh to $160 per MWh by 2025, even as their life cycle increases. Such advances in energy storage have the potential to make battery-powered vehicles cost competitive. When used in the electric grid to improve reliability, reduce outages, and enable distributed generation, energy storage can dramatically improve the efficiency of our utility grids and bring electricity to remote and underserved areas around the world.22 3.Machines working for us. Industrial automation has been around for several decades, and the robots on the factory floor are now changing fast.

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International Energy Agency, September 2014, www.iea.org/newsroomandevents/pressreleases/2014/september/how-solar-energy-could-be-the-largest-source-of-electricity-by-mid-century.html. 60. Thomas G. Kreutz and Joan M. Ogden, “Assessment of hydrogen-fueled proton exchange membrane fuel cells for distributed generation and cogeneration,” Proceedings of the 2000 US DOE Hydrogen Program Review, US Department of Energy, October 2000. CHAPTER 7. END OF AN ERA 1. “Elevated rail corridor in Mumbai: Project information memorandum,” Indian Railways, www.indianrailways.gov.in/railwayboard/uploads/directorate/infra/downloads/Project_Information_Memorandum.pdf. 2.

Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals
by David Aronson
Published 1 Nov 2006

In contrast, the MC sampling distribution’s mean is the expected return of a useless rule in a data-mining venture. If, after randomly assigning rule values to market returns, the sampling distribution is situated at a value greater than zero, so be it. pvalues can be computed directly from the MC generated sampling distribution, just as they would be with a sampling distribution generated by alternative methods. The p-value is the area in the right tail of the distribution that lies at or beyond the back-tested rule’s mean return. A limitation of the MC method compared to WRC is that it cannot be used to generate confidence intervals because it does not test a hypothesis about the rule’s mean return.

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See also Data mining; Data-mining bias computer-intensive methods applied to single rule, 241–243 data mining versus single-rule, 268–271 position bias and market trend components, 23–27 Bacon, Francis, 125–126 Barberis, Shleifer, and Vishny (BSV) hypothesis, 372–374, 376 Baseball statistics, data-mining bias and, 258 Base rate fallacy, 91 Bayes’ theorem, 90, 346 Beads in a box sampling example, 172–186 sampling distribution and, 203 statistical theory elements and, 186–190 Behavioral finance theory, 355–378 foundations of, 356–357 psychological factors, 357–362 scientific hypotheses of, 371–378 self-organizing Ponzi schemes and, 370–371 social factors, 362–369 Behaviorism, 81–82 Beliefs: belief inertia, in behavioral finance theory, 375 contrasted to knowledge, 1–5 erroneous, 33–38 Bell curve, 211–213 517 518 Bellman, Richard, 465 Benchmarks, 22–29 detrending and, 27–28 effect of position bias and market trend on, 23–27 using logs instead of percentages, 28–29 Best-performing rule: data-mining bias and, 263–264, 278–287 defined, 256 Biases, see Subjective technical analysis Bible Codes, data-mining bias and, 258–260 Binary rules, 15–31 inputs, outputs, signals, 16, 17 look-ahead bias, 29–30 subjective technical analysis and, 15–16, 72–78 thresholds and: fixed, 16–19 multiple, 19–21 trading costs, 31 traditional and inverse rules, 21–22 use of benchmarks in evaluation, 22–29 Black, Fischer, 345 Bloom, Norman, 258 Bootstrap sampling, 215, 235–238 applied to back test of single rule, 241–242 confidence intervals and, 248–250 contrasted to Monte Carlo method, 235 data-mining bias solutions and, 320–330 Bostian, David, 409 Bounded rationality, principle of, 42 INDEX Box Theory, 36–37 Bulkowski, T.N., 161 Camerer, C., 466–467 Capital asset pricing model (CAPM), 340–341 Case study, see Rule data mining case study Categorical syllogisms, 112–115 Central Limit Theorem, 211–213 Central tendency measurements, 191 Chaiken, Marc, 409 Chang, Kevin, 151–161 Channel breakout operator (CBO), 397–398, 419–420 Channel-normalization operator (CN), 401–403 Chart analysis, misplaced faith in, 82–86 representativeness heuristic and illusory trends and patterns, 93–101 Clustering illusion, 99–100, 362 Cognitive content, of knowledge and beliefs, 2–5 Cognitive psychology, see Subjective technical analysis Cohen, P.R., 282 Cointegration, 434–436 Commodity and currency hedge risk transfer premium, 379, 380–384 Complex rules, not in case study, 392, 452–461 Computer-based sampling methods, 215, 234–243 human interaction with, 464–465, 471–473 Conditional probability (p-value), 231–233 Index Conditional syllogisms, 115–116 invalid forms, 118–121 valid forms, 117–118 Confidence interval, 243, 245–247 defined, 216 generating with bootstrap method, 248–250 hypothesis test contrasted to, 250–252 sampling distribution and, 247–248 for TT-4-91 rule, 252–253 Configural-thinking problems, 42–45 Confirmation bias, 62–71 behavioral finance theory and, 358 belief survival and, 69 contradictory evidence and, 67–69 perception and motivation and, 62–63 questions and search and, 63–64 subjective methods and, 69–71 vague evidence and, 66–67 value evaluation criteria and, 64–66 Conjunction fallacy, 91–93 Conservatism bias: behavioral finance theory and, 357–358 BVS hypothesis and, 372–374 DHS hypothesis and, 375–376 Consistency, rule of, 111–112 Control illusion, 50 Cooper, Michael, 353, 384 Correlations, illusory, 72–82 asymmetric binary variables and, 78–80 behavioral psychology and, 81–82 binary variables and, 72 519 hidden or missing data and, 80 possible outcomes of binary variables and faulty intuition, 73–78 Cowles, Alfred, 462–463 Cumulative sum price-volume functions, 407–413 Curse of dimensionality, 465 Cutler, D., 349 Daniel, Hirshleifer, and Subrahmanyam (DHS) hypothesis, 375–376 Darvas, Nicholas, 36–37 Data distribution of the population, 206–207 Data distribution of the sample, 206–207 Data mining, see also Data-mining bias; Rule data mining case study confirmation bias and, 64 defined, 171, 255, 256, 264 as multiple comparison procedure, 264–265 soundness of premise of, 309–311 as specification search, 265–267 Data-mining bias, see also Data mining; Rule data mining case study anecdotal examples of, 256–261 causes of, 263–264, 278–287 defined, 255–256 experimental investigations of, 291–320 ATRs and, 309–311 variable merit rules and, 311–320 factors determining magnitude of, 287–291 objective technical analysis and, 267–272 520 Data-mining bias (Continued) solutions for dealing with, 320–330 statistical inference and, 272–278 Data-snooping bias, 390–391, 449 Dawes, R.M., 468–469 Declarative statements, beliefs and knowledge and, 2–5 Deductive logic, 112–121 categorical syllogisms, 112–115 conditional syllogisms, 115–121 Denial of the consequent, 112–121, 170, 219, 221 Descartes, Rene, 126 Detrending, 19, 27–28 in case study, 391–392 proof of value of, 475–476 Diaconis, Percy, 260 Directional modes, 21 Discernible-difference test, cognitive content and, 3–4 Divergence rules, tested in case study, 430–440 Drosnin, Michael, 259, 260 Dysart, Paul, 411, 412–413 Efficient Markets Hypothesis (EMH), 331, 334–355 assumptions of, 343–345 flaws in, 345–348 consequences of market efficiency, 335–336 efficient markets defined, 334–335 empirical challenges to, 349–355 evidence of, 337–342 false notions of, 337 falsification and, 141–143 information content of hypotheses and, 137–138 nonrandom price motion and, 378–385 paradoxes of, 342–343 INDEX Ehlers, J.F., 399, 400, 452 Einstein, Albert, 108 Elder, John, 83 Elfron, B., 235 Elliott Wave Principle (EWP), 60–61, 69–70, 137 Endowment bias, 375–376 Engle, R.F., 434, 436 Enumeration, deduction by, 122–124 Equity market risk premium, 379 Error-correction model, 434–435 Errors, unbiased and systematic, 272–274 Euler circles, 114–115 Evidence, vague and contradictory, 64–69 Evidence-based technical analysis (EBTA), see also Rule data mining case study academic findings and, 8–9 defined, 1 differs from technical analysis, 6–8 future and, 463–465, 471–473 Expected performance, defined, 255 Extended Middle, Law of, 111 Extreme values and transitions (E rules), tested in case study, 420–430 Falsifiable forecast, 47, 58 Falsificationism, 130–136 information content and, 136–139 scientific responses to, 139–143 Falsification of the consequent, 219–221 Fama, Eugene, 339, 354–355 Feedback, behavioral finance theory and, 366–371 Felsen, J., 464 Index Festinger, L., 63 Finucase, M., 470 Flip-flop(s), 20, 361 Forgas, Joseph, 471 Fosback, Norman, 411, 412–413, 416, 436, 464 Fosback Index, 436 French, Kenneth, 354–355 Frequency distribution, 179–181, 190–191 measuring central tendency, 191 variability (dispersion) measurements, 192–193 Futures markets, 380–384 Galileo Galilei, 106–107 Gambler’s fallacy, 99–100, 362 Gilovich, Thomas, 38, 68, 80, 85 Goldberg, L.R., 467 Gould, Stephen Jay, 58 Granger, C.W.J., 434, 436 Granville, Joseph, 408 Grossman, S.J., 343, 378 Grove, W.M., 468 Hall, J., 3–4 Hansen, Peter, 329 Harlow, C.V., 413 Hastie, R., 468–469 Hayes, T., 21 Head-and-shoulders pattern, objectification example, 151–161 Heiby, W.A., 403, 433 Herd behavior, 362–369 Heuristic bias, 41, 86–87 availability heuristic, 87–88 heuristic defined, 86 representativeness heuristic, 88–93 illusory trends and patterns and, 93–101 521 Hindsight bias, 50–58 Hong and Stein (HS) hypothesis, 376–377 Hsu, P.-H., 451, 455 Hulbert Digest, 48 Hume, David, 126–128 Hussman, J., 430 Hypotheses, see also Hypothesis tests alternative, 221–225, 394 development of concept of, 128–130 falsifiability and, 130–143 null, 139, 166–172, 221–225, 393 Hypothesis tests: computer-intensive methods of sampling distribution generation, 234–243 confidence intervals contrasted to, 250–252 defined, 217–218 informal inference contrasted, 218–223 mechanics of, 227–234 rationale of, 223–227 Hypothetico-deductive method: stages of, 144–147 technical analysis example, 145–146 Illusory correlations, 72–82 asymmetric binary variables and, 78–80 behavioral psychology and, 81–82 binary variables and, 72 hidden or missing data and, 80 possible outcomes of binary variables and faulty intuition, 73–78 Illusory knowledge, 41–42, 49–50 Imitative behavior, 362–369 522 Immediate practical future: in case study, 393 defined, 186–187 Indicators, in case study, 405–417 interest rate spreads, 417 market breadth indicators, 413–416 price and volume functions, 406–413 prices-of-debt instruments from interest rates, 416–417 Indicator scripting language (ISL), 403–405 Inductive logic, 121–124.

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See also Nonrandom price motion theories Random variables, defined, 175 Random walks, see Efficient Markets Hypothesis Rational investor assumption, of Efficient Markets Hypothesis, 343–346 Rationality, limits of, 356–357 Reasoning by representativeness, 87–88 Reinforcement schedule, illusory correlations and, 81–82 Relative frequency distribution, 181–186, 197–202 Relative Strength Index (RSI), 460 Representativeness heuristic, 88–93, 93–101 525 Reversal rules, defined, 17 Risk, defining and quantifying, 340–341 Risk transfer premiums, 378–385 Roberts, Henry, 83 Roll, R., 349 Romano, J.P., 330 Rule data mining case study: critique of, 448–451 indicators used in, 405–417 interest rate spreads, 417 market breadth indicators, 413–416 price and volume functions, 406–413 prices-of-debt instruments from interest rates, 416–417 parameters of, 389–392 possible extensions of, 451–461 raw time series used in, 405–406, 417–418 results of, 441–448 rules tested in: divergence, 430–440 extremes and transitions, 420–430 trend rules, 419–420 in statistical terms, 392–394 time-series operators in, 396–405 channel breakout operator, 397–398 channel-normalization operator, 401–403 indicator scripting language, 403–405 moving-average operator, 398–401 transforming data series into market positions, 394–396 Rules, see Binary rules; Rule data mining case study Russell, Bertrand, 59, 166 Russo, J.E., 467 526 S&P 500, see Rule data mining case study Sagan, Carl, 40–41 Sample mean, 191, 243–245 Sample size neglect, 361–362, 372–374 Sampling, see also Sampling distribution beads in a box example of, 172–186 frequency distribution and, 179–181 relative frequency distribution, 181–186 sample statistics, 175–177, 188–189, 202, 393 sampling variability, 177–179 Sampling distribution, 201–202 classical derivation approach, 209–215 computer-intensive methods of generating, 215, 234–243, 464–465, 471–473 confidence intervals and, 247–248 data mining and, 276–278 defined, 203 mechanics of hypothesis testing and, 227–234 sampling distribution of the mean, 209–213 trading performance and, 206 uncertainty qualified by, 203–206 Samuelson, Paul, 335–336 Sawyer, J., 466 Schoemaker, P.J.H., 467 Scientific method: defined, 103, 332 history of, 103–108 hypothetic-deductive method, 144–147 key aspects of, 147–148 logic and, 111–124 INDEX nature of scientific knowledge and, 108–110 objectification of subjective technical analysis, 148–151 example, 151–161 openness and skepticism in, 143, 225 philosophy of, 124–143 search bias and, 64 Secondhand information bias, 58–61 anchoring and, 360–361 information diffusion and, 365–366 Self-attribution bias, 48–49 DHS hypothesis and, 375–376 Self-interest, secondhand accounts and, 61 Shermer, Michael, 38 Shiller, Robert, 333–334, 365, 366 Shleifer, Andre, 347 Siegel, Jeremy, 84 Signals, 16–18 Simon, Barry, 259 Simon, Herbert, 42 Simplicity, principle of, 107–108, 225–227 Single-rule back-testing, versus data mining, 268–271 Skepticism, 143, 225 Slope of yield curve, 417 Slovic, Paul, 41, 470 Snelson, Jay Stuart, 71 Socioeconomics, 151 Spatial clustering, 100–101 Stale information, 340, 349, 351–354 Standard deviation, 192 Standard error of the mean, 213–215 Statement about reliability of inference, 190 Index Stationary statistical problems, 174, 188 Stationary time series, 19 Statistical analysis: descriptive statistics tools: central tendency measurements, 191 frequency distribution, 190–191 variability (dispersion) measurements, 192–193 inferential statistics: elements of statistical inference problem, 186–190 sampling example, 172–186 three distributions of, 206–207 probability, 193 Law of Large Numbers, 194–195 probability distribution, 200–202 probability distribution of random variables, 197–199 theoretical versus empirical, 196 sampling distribution and, 201–206 classical derivation approach, 209–215 computer-intensive approach, 215 used to counter uncertainty, 165–172 Statistical hypothesis, defined, 220 Statistical inference: data mining and, 272–278 defined, 189 hypothesis tests: computer-intensive methods of sampling distribution generation, 234–243 confidence intervals contrasted to, 250–252 527 defined, 217–218 informal inference contrasted, 218–223 mechanics of, 227–234 rationale of, 223–227 parameter estimation: defined, 217–218 interval estimates, 218, 243, 245–253 point estimates, 218, 243–245 Statistical significance, 23 in case study, 394 statistical significance of observation, 171 statistical significance of test (p-value), 232–234 Stiglitz, J.E., 343, 378 Stochastics, 401–403 Stories, see Secondhand information bias Subjective technical analysis, 5–8, 15–16, 161–163 adoption of scientific method and, 148–151 example, 151–161 chart analysis and, 82–86 confirmation bias and, 62–71 erroneous beliefs and, 33–35 futility of forecasting and, 465–471 heuristic bias and, 86–93 illusion trends and chart patterns, 93–101 human pattern finding and information processing, 39–45 illusory correlations and, 72–82 overconfidence bias and, 45–58 secondhand information bias and, 58–61 as untestable and not legitimate knowledge, 35–38 528 Syllogisms: categorical, 112–115 conditional, 115–116 invalid forms, 118–121 valid forms, 117–118 Taleb, Nassim, 337 Technical analysis (TA), 9–11.

Taming the Sun: Innovations to Harness Solar Energy and Power the Planet
by Varun Sivaram
Published 2 Mar 2018

Pieter Gagnon, Robert Margolis, Jennifer Melius, Caleb Phillips, and Ryan Elmore, “Rooftop Solar Photovoltaic Technical Potential in the United States: A Detailed Assessment,” National Renewable Energy Laboratory (NREL), 2016, http://www.nrel.gov/docs/fy16osti/65298.pdf. 31.  Travis Lowder, Paul Schwabe, Ella Zhou, and Douglas J. Arent, “Historical and Current U.S. Strategies for Boosting Distributed Generation,” National Renewable Energy Laboratory (NREL), 2015, http://www.nrel.gov/docs/fy16osti/64843.pdf. 32.  Solar Energy Industry Association (SEIA), “Expanding Solar Deployment Opportunities in the C&I Sector,” November 2016, http://www.seia.org/sites/default/files/resources/SEIA-CPACE_Expanding_Solar_Deployment_CI_Sector_April2017.pdf. 33.  

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Enrique Santacana, Gary Rackliffe, Le Tang, and Xiaoming Feng, “Getting Smart,” IEEE Power and Energy Magazine 8, no. 2 (2010): 41–48, doi:10.1109/mpe.2009.935557. 38.  Ryan Hanley, “A Pathway to the Distributed Grid,” SolarCity White Paper, February 2016, http://www.solarcity.com/sites/default/files/SolarCity_Distributed_Grid-021016.pdf. 39.  S. Abdi and K. Afshar, “Application of IPSO-Monte Carlo for Optimal Distributed Generation Allocation and Sizing,” International Journal of Electrical Power & Energy Systems 44, no. 1 (2013): 786–797. 40.  Stephen Lacey, “Microsoft Says ‘Computational Demand Response’ Could Lower Data Center Emissions 99%,” Greentech Media, June 27, 2013, https://www.greentechmedia.com/articles/read/Microsoft-Says-Computational-Demand-Response-Could-Lower-Data-Center-Emis. 41.  

Mastering Machine Learning With Scikit-Learn
by Gavin Hackeling
Published 31 Oct 2014

Jaccard similarity is calculated by the following equation: J ( Predicted,True ) = Predicted ∩ True Predicted ∪ True >>> import numpy as np >>> from sklearn.metrics import hamming_loss >>> print hamming_loss(np.array([[0.0, 1.0], [1.0, 1.0]]), np.array([[0.0, 1.0], [1.0, 1.0]])) 0.0 >>> print hamming_loss(np.array([[0.0, 1.0], [1.0, 1.0]]), np.array([[1.0, 1.0], [1.0, 1.0]])) 0.25 >>> print hamming_loss(np.array([[0.0, 1.0], [1.0, 1.0]]), np.array([[1.0, 1.0], [0.0, 1.0]])) 0.5 [ 94 ] www.it-ebooks.info Chapter 4 >>> print jaccard_similarity_score(np.array([[0.0, 1.0], [1.0, 1.0]]), np.array([[0.0, 1.0], [1.0, 1.0]])) 1.0 >>> print jaccard_similarity_score(np.array([[0.0, 1.0], [1.0, 1.0]]), np.array([[1.0, 1.0], [1.0, 1.0]])) 0.75 >>> print jaccard_similarity_score(np.array([[0.0, 1.0], [1.0, 1.0]]), np.array([[1.0, 1.0], [0.0, 1.0]])) 0.5 Summary In this chapter we discussed generalized linear models, which extend ordinary linear regression to support response variables with non-normal distributions. Generalized linear models use a link function to relate a linear combination of the explanatory variables to the response variable; unlike ordinary linear regression, the relationship does not need to be linear. In particular, we examined the logistic link function, a sigmoid function that returns a value between zero and one for any real number.

CIOs at Work
by Ed Yourdon
Published 19 Jul 2011

In addition, the analytics can be shared with out call center. The customers don’t even have to call us. We can call them and say, “A pole in your area has been damaged. Crews are on site and repair should be complete soon.” All of that is coming in the very near future. And the other thing is that in the future there will be a lot more distributed generation. For example, there may be customers that are generating their own power through solar panels. We’re putting smart meters on everyone’s home so we can tell them—in the future—how much electricity they’re using at different times of the day. If customers want to be more efficient, these smart meters have the intelligence to tell them when they’re using a lot and determine what it is that’s driving usage up.

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Index A AdKnow ledge, Inc., 87 Agile development methodology, 62 Amazon, 314, 319 America COMPETES Act, 304 American Airlines, 47, 72 American Defense Department, 84 American Marketing Society, 113 American Production Inventory Control Society, 211 Ames, 320 AMR Corp, 47 Android, 43 Annapolis, 340 Apple, 97, 101, 217, 242, 295 Computer, 35 Genius Bar, 8 Archipelago Holdings Inc., 87 Arizona Public Service (APS) Company, 66, 211, 223 Arizona State University, 227 ARPANET, 19, 117, 135 Art of Computer Programming, 2 Atlanta-based Southern Company, 191 AT&T, 191, 249 B Ballmer, Steve, 39 Bank of Boston, 47 Baylor-Grapevine Board of Trustees, 47 Bedrock foundation, 249 Bell Atlantic Mobile, 231 Bell Labs, 2, 249 BlackBerry, 60, 96, 116, 121, 171, 184, 246, 261, 296, 317 Blalock, Becky, 182, 191, 215 adaptability, 192 Air Force brat, 191, 192 Atlanta-based Southern Company, 191 banking industry, 203 Boucher, Marie, 196 brainstorm, 202 24/7 business, 199 business intelligence, 204 cloud computing, 205 cognitive surplus, 206 cognitive time, 206 Coker, Dave, 196 communication and education, 200 Community and Economic Development, 194 consumer market, 202 cybersecurity, 207, 209 data analytics, 204, 205 disaster recovery, 209 distributed generation, 204 distribution organization, 201 Egypt revolution, 198 farming technology, 206 finance backgrounds/marketing, 200, 209 Franklin, Alan, 193 Georgia Power, 191 Georgia Power Management Council, 193 global society, 206 Google, 198 incredible technology, 195 Industrial Age, 206 Information Age, 206 InformationWeek's, 196 infrastructure, 202 intellectual property, 196 intelligence and redundancy, 207 Internet, 198, 206 leapfrog innovations, 205 mainframe system, 207 marketing and customer service, 193, 200 MBA, finance, 192 microfiche, 207 microwave tower, 207 mobile devices, 203 mobility and business analytics, 205 Moore's Law, 205 new generation digital natives, 197 flexible and adaptable, 199 innovation and creativity, 199 superficial fashion, 198 Olympic sponsor, 193 out pushing technology, 202 reinforcement, 201 sense of integrity, 200 Southern Company, 194, 198, 201, 207 teamwork survey, 201 technology lab, 202 undergraduate degree, marketing, 192 virtualization, 205 VRU, 203 Ward, Eileen, 196 wire business, 201 world-class customer service, 203 Bohlen, Ken, 211 American Production Inventory Control Society, 211 Apple, 217 APS, 211, 223 ASU, 227 benchmarking company, 216 chief innovation officer, 229 Citrix, 217 cloud computing, 218, 219 cognitive surplus, 220 DECnet, 212 Department of Defense, 222 distributed computing, 217 energy industry, 214 gizmo/whiz-bang show, 216 GoodLink, 217 hard-line manufacturing, 218 home computing, 219 home entertainment, 219 Honeywell, 219 HR generalists, 215 information technology department, 211 Intel machines, 217 John Deere, 213 just say yes program, 223 Lean Six Sigma improvement process, 211 Linux, 220 MBA program, 214 mentors, 213 national alerts, 224 North American universities, 228 paradigm shifts, 218, 220 PDP minicomputers, 212 Peopleware, 226 prefigurative culture, 221 R&D companies, 218 Rhode Island, 226 role models, 213 San Diego Fire Department, 224 security/privacy issues, 217 skip levels, 223 smart home concepts, 219 smartphone, 217 social media, 225 Stead, Jerry, 214 Stevie Award, 211 Storefront engineering, 212 traditional management, 219, 226 Twitter, 224 vocabulary, 221 Waterloo operations, 213 Web 2.0 companies, 227 Web infrastructure, 215 wikipedia, 220 Y2K, 222 Botnets, 23 Brian's and Rob Pike's, 2 Bristol-Myers Squibb, 33 Broadband networks, 241 Brown, 227 Bryant, 227 BT Global Services, 253 BT Innovate & Design (BTI&D), 253 Bumblebee tuna, 130 C Career writing technology, 67 CASE tools, 232 Cash, Jim, 50 Christensen, Clyde, 212 Chrome, 14, 18 Chrysler Corporation, 175 Citibank, 337 Citicorp, 313 Citrix, 217 Client-server-type applications, 59 Cloud computing, 218, 219, 239, 240, 261, 262, 310, 311, 313 Cloud technology, 62 CNN, 54 COBOL, 250 Cognitive surplus, 20, 79, 206, 291 College of Engineering, University of Miami, 113 Columbia University, 1 Community and Economic Development, 194 Computer Sciences Corporation, 35 Computerworld magazine, 196 Consumer-oriented technology, 22 Content management system, 133 Corporate information management (CIM) program, 309 Corporate Management Information Systems, 87 Corvus disk drive, 36 Customer Advisory Boards of Oracle, 191 Customer-relationship management (CRM), 56 Cutter Business Technology Council, 173 D Dallas Children's Medical Center Development Board, 48 DARPA, 19 DDoS attacks and security, 81 DECnet, 212 Dell Platinum Council, 113 DeMarco, Tom, 16, 226 Department of Defense, 222, 329, 332 Detroit Energy, 252 Digital books, 30 Digital Equipment, 48 Distributed computing, 217 Dodge, 189 Dogfooding, 11, 37, 38, 236 DTE Energy, 173 DuPont Dow Elastomers, 151 E Educational Testing Service (ETS), 151 E-government, 282, 285 Electrical distribution grid, 182 Elementary and Secondary Education Strategic Business Unit, 151 Elements of Programming Style, 2 Ellyn, Lynne, 173 advanced technology software planning, 175 Amazon, 184 artificial intelligence group, 175 Association for Women in Computing, 173 benchmark, 180, 181 BlackBerries, 184 Burns, Ursula, 175 Chrysler, 176 Cisco, 186 cloud computing, 183, 184 component-based architecture, 186 corporate communications customer service, 185 Crain's Detroit Business, 173 cyber security threats, 177 degree of competence, 187 diversity and sophistication, 182 DTE Energy, 173 energy trading, 176 engineering and science programs, 188 enterprise business systems policy, 186 executive MBA program, 176 Facebook, 185 fresh-out-of-the-university, 187 General Electric, 174 Google, 184 Grace Hopper, 174 grid re-automation, 182 Henry Ford Hospital, 174 internal social media, 185 International Coaching Federation, 178 iPads, 184 IP electrical grids, 182 iPod applications, 182 IT budgets, 186 IT responsibilities, 176 Java, 186 level of sophistication, 179 lobbying efforts, 181 medical computing, 175 Miller, Joan, 174 Mulcahey, Anne, 175 Netscape, 175 neuroscience leadership, 189 object-oriented programming, 186 Oracle, 186 peer-level people, 179 people system, 177 policies and strategies, 180 Radio Shack, 180 remote access capacity, 189 security tool and patch, 183 sense of community, 180 Shipley, Jim, 174 smart grid, 177, 182 smart meters, 182 smart phone applications, 183 swarming, 179 technical competence, 178, 179 Thomas, Marlo, 174 Twitter, 185 UNITE, 181 vendor community, 186 virtualization, 183, 184 Xerox, 175 E-mail, 9 Employee-relationship management (ERM), 56 Encyclopedia, 115 Encyclopedia Britannica, 292 ERP, 123 F Facebook, 244 Ellyn, Lynne, 185 Sridhara, Mittu, 73, 84 Temares, Lewis, 116, 121, 131 Wakeman, Dan, 169 Federal information technology investments, 299 Flex, 236 Ford, 102 Ford, Monte, 47 agile computing, 59 agile development, 62, 66 airplanes, 51 American Airlines, 47 Arizona Public Services, 66 Bank of Boston, 47 Baylor-Grapevine Board of Trustees, 47 BlackBerry, 60 board of Chubb, 51 board of Tandy, 51 business organizations, 63 business school, dean, 50 career writing technology, 67 client-server-type applications, 59 cloud technology, 62 CNN, 54 common-sense functionality, 49 consumer-based technology, 60 CRM, 56 Dallas Children's Medical Center Development Board, 48 Digital Equipment, 48 ERM, 56 financial expert, 69 frequent-flier program, 57 frontal lobotomy, 57 Harvard Business Review, 50 HR policies, 65 IBM, 48 information technology, 47, 52 Internet, 54 Internet-based protocol, 59 iPhone, 52 IT stuff, 58 Knight Ridder, 51 legacy apps, 59 mainframe-like applications, 59 management training program, 64 marketing and technical jobs, 48 Maynard, Massachusetts mill, 48 MBA program, 50 mentors, 49 Microsoft, 50 mobile computing, 62 New York Times, 53 operations center, 54 PDP-5, 49 PDP-6, 49 Radio Shack, 51 revenue management, 57 role models, 49 security paradigms, 62 self-service machine, 57 Silicon Valley companies, 68 smartphones, 54 social networking, 51, 53, 56, 58 stateful applications, 59 techie department, 48 The Associates First Capital Corporation, 47 transmission and distribution companies, 47 wireless network, 59 YouTube, 65 Fort Worth, 226 Free software foundation, 19 Fried, Benjamin, 1, 241 agile development, 25 agile methodologies, 26 Apple Genius Bar, 8 ARPANET, 19 Art of Computer Programming, 2 Bell Labs, 2 books and records, accuracy, 25 botnets, 23 Brian's and Rob Pike's, 2 cash-like principles, 29 CFO, 4 check writers, 18 chrome, 14, 18 classic computer science text, 1 cognitive surplus, 20 Columbia University, 1 compensation management, 7 competitive advantage, 9, 18 computer science degree, 1 computer scientists, 6 consumer-driven computing, 12 consumer-driven software-as-a-service offerings, 12 consumer-driven technology, 12 consumer-oriented technology, 14, 22 corporate leadership, 25 cost centers, 4 DARPA, 19 decision makers, 17 decision making, 13 360-degree performance management, 7 detroit energy, 30 digital books, 30 document workbench, 2 dogfooding, 11 e-books, 29 Elements of Programming Style, 2 e-mail, 9 end-user support, 7 engineering executive group, 4 European vendors, 6 file servers and print servers, 17 Folger Library editions, 30 free software foundation, 19 German company, 13 German engineering, 13 Gmail, 15 Godot, 26 Google, 1 books, 29 products, 5, 10 software engineers, 6 hiring managers, 6 HR processes and technologies, 6 IBM model, 13 instant messaging, 9 Internet age, 6 interviewers, training, 6 iPad, 29 iPhone, 29 IPO, 3 IT, engineering and computer science parts, 4 Knuth's books, 2 Linux machine, 8 Linux software, 19 machine running Windows, 8 Macintosh, 8 Mac OS, 9 macro factors, 11 Managing Director, 1 mentors, 1 microcomputers, 18 Microsoft, 5 Minds for Sale, 20 Morgan Stanley, 1–3, 5, 16 nonacademic UNIX license, 2 nontechnical skills, 5 oil exploration office, 17 open-source phone operating system, 20 outlook, 15 PARC, 19 performance review cycles, 7 personal computer equipment, 15 post-Sarbanes-Oxley world, 25 project manager, 13 quants, 24 rapid-release cycle, 26 R&D cycle, 24 regression testing, 27 role models, 1 shrink-wrapped software, 14 signature-based anti-virus, 22 smartphone, 20, 27 social contract, 8 society trails technology, 21 software engineering tool, 13 software installation, 14 supply chain and inventory and asset management, 10 SVP, 4 telephony, 17 ten things, 13 TMRC, 19 TROFF, 2 typesetter workbench, 2 UI designer, 14 university computing center, 28 videoconferencing, 12 Visicalc, 24 Wall Street, 23 Walmart, 6 waterfall approach, 25 XYZ widget company, 5 YouTube video, 20 G Gates, Bill, 39, 50 General Electric, 134 General Foods, 309, 326–328 General Motors, 33, 321, 329, 332 George Mason School of Information Technology, 309 Georgia Power Company, 191–193, 196 Georgia Power Management Council, 193 German company, 13 German engineering, 13 German manufacturing company, 232 Gizmo/whiz-bang show, 216 Gmail, 15 GoodLink, 217 Google, 1, 84, 85, 117, 217, 219, 220, 222, 235, 241, 263, 302, 319 apps, 314 books, 29 commercial products, 10 model, 293 Government Accountability Office (GAO), 305 4G program, 250 4G smartphone, 235 GTE, 231 Gupta, Ashish aspiration, organization, 256 bandwidth and network infrastructure, 267 BlackBerry, 261 business and customer outcomes, 274 capital investment forums, 269 career progression, 255 cloud-based shared infrastructure model, 263 cloud computing, 261, 262 collaboration, 272 communications infrastructure, 258 compute-utility-based model, 262 control and integrity, 268 core competency, 255 core network infrastructure, 267 core strengths, 256 cost per unit of bandwidth, 267 customer demands, 268 data protection, 261, 262 decision-making bodies, 269 demographics, 272, 273 device convergence, 263 dogfooding, 259 employee flexibility, 260, 264 engagement and governance, 269 enterprise market segment, 261 equipment management, 260 executive MBA, 256 fourth-generation LTE networks, 267 functional service departments, 270 Global Services, distributed organization, 257 Google, 263, 275 Google Apps, 266 handheld devices, 265 hastily formed networks, 258 IMF, 266 innovation and application development, 265 iPad, 257, 260, 261, 266,267 iPhone, 266 Japan, 257, 258 London Business School, 253 management functions, 257 management sales functions, 257 market segments, 259 MBA, General Management, 253 measurements, 271 messaging with voice capability, 264 mini-microcomputer model, 261 mobile communications network, 258 mobile-enabling voice, 259 mobile phone network, 260 mobile traffic explosion, 265 network infrastructures, 265 network IT services, 254 network quality, 257 new generation digital natives, 271 disadvantages, 273 Google, 273 opportunities, 273 Olympics, 263 opportunities, 275 organizational construct, 272 outsourced network IT services, 259 outsourcing, 271 per-use-based model, 262 portfolio and business alignment, 274 Portfolio & Service Design (P&SD), 253 primary marketing thrust, 264 product development thrust, 264 product management team, 259 project and program management, 255 resource balance, 270 scalability, 262 security, 262 Selley, Clive, 254, 255 service delivery organization, 254 single-device model, 264 smart devices, 267 smart phones, 266 telecommunications capability, 259 upward-based apps, 264 virtualization, 261 voice-over-IP connections, 258 Windows platform, 261 Gurnani, Roger, 231 accounting/finance department, 233 analog cellular networks, 250 AT&T, 249 bedrock foundation, 249 Bell Atlantic Mobile, 231 Bell Labs, 249 blogs, 244 broadband networks, 241 business benefits, 237 business device, 240 business executives, 238 business leaders, 248, 249 business relationship management, 248 buzzword, 239 CASE tools, 232 cloud computing, 239, 240 COBOL, 250 consumer and business products, 231 consumer electronics devices, 241 consumer telecom business, 233 customer-engagement channel, 244 customer forums, 244 customer support operations, 251 customer-touching channels, 236 degree of control, 246 distribution channel, 250 dogfooding, 236 ecosystem, 243, 249 enterprise business, 233 ERP systems, 236 face-to-face communications, 244 FiOS product, 235 flex, 236 "follow the sun" model, 239 German manufacturing company, 232 4G program, 250 4G smartphone, 235 hardware/software vendors, 247 information assets, 245 information technology strategy, 231 intellectual property rights, 244 Internet, 235, 239 iPhone, 243 Ivan, 232 Lowell, 232 LTE technology-based smartphone, 235 marketing, 251 MIT, 246 mobile technology, 234 Moore's law, 242 MP3 file, 235 network-based services, 240 Nynex Mobile, 233 P&L responsibility, 251 PDA, 238 personal computing, 235 product development, 234, 251 role models, 232 sales channels, 251 smartphones, 238 state-level regulatory issues, 251 state-of-the-art networks, 243 telecom career, 232 telephone company, Phoenix, 234 Verizon Communication, 231, 232 virtual corporations, 241 Web 2.0, 244 Williams Companies, 232, 233 WillTell, 233 wireless business, 233 H Hackers, 19 Harmon, Jay, 213 Harvard Business Review, 50 Harvard Business School, 331 Heller, Martha, 171 Henry Ford Hospital, 174 Hewlett-Packard piece, 129 Home computing, 219 Honda, 102 Honeywell, 219 Houghton Mifflin, 134, 136 I IBM, 48, 250 manpower, 311 model, 13 Indian IT outsourcing company, 255 Information technology, 52 Intel machines, 217 International Coaching Federation, 178 Internet, 9, 44, 54, 117, 235, 239, 316, 322 Internet-based protocol, 59 Interoperability, 341 iPads, 2, 94, 97, 184, 257, 260, 264, 267, 288, 289, 295, 296 IP electrical grids, 182 iPhones, 43, 52, 96, 101, 170, 181, 260, 264,296 iPod, 101 IT lifecycle management process, 37 Ivan, 232 J John Deere, 213 K Kansas, 226 Kernigan, Brian, 2 Knight Ridder, 51 Knuth, Donald, 2, 29 Kraft Foods Inc, 309 Krist, Nicholas, 28 Kundra, Vivek Clever Commute, 305 cognitive surplus, 303 command and control systems, 301 consumerization, 302 consumption-based model, 300 cyber-warfare, 301 Darwinian pressure, 302 desktop core configuration, 306 digital-borne content, 301 digital oil, 300, 307 digital public square, 304 enterprise software, 303 entrepreneurial startup model, 306 frugal engineering, 306 Google, 302 government business, 302 innovator's dilemma, 307 iPad, 302 IT dashboard, 302 leapfrog technology, 306 massive consumerization, 301 megatrends, 301 parameter security, 302 Patent Office, 305 pharmaceutical industry, 304 phishing attacks, 301 policy and strategic planning, 299 security and privacy, 301 server utilization, 300 social media and technology, 300, 306 storage utilization, 300 Trademark Office, 305 Wikipedia, 303 L LAN, 259 Lean Six Sigma improvement process, 211 Levy, Steven (Hackers), 19 Linux, 220 machine, 8 open-source software, 19 Lister, Tim, 226 London Business School, 73, 253, 256 Long-term evolution (LTE), 235 Lowell, 232 M MacArthur's intelligence officer, 327 Macintosh, 8 Mainframe computers, 118 Mainframe-like applications, 59 Marriott's Great America, 35 McDade, 327 McGraw-Hill Education, 133, 147, 150 Mead, Margaret, 221 Mendel, 311 Microcomputers, 18 Microsoft Corporation, 5, 11, 33, 36, 38, 41, 44, 46, 50, 156, 217, 223, 236, 250, 293 Microsoft Higher Education Advisory Group, 113 Microsoft's operational enterprise risk management, 33 Middlesex University, 189 Miller, Joan Apple products, 295 authority and accuracy, 292 award-winning ICT programs and services, 277 back locked-down information, 289 big-scale text issues, 294 big-time computing, 279 BlackBerry, 296 business management training, 281 business skills, 281 central government, 283 cognitive surplus, 291 community care project, 278 community development programs, 277, 278 computers, constituency office, 294 confidential information, 284 data management, 281 decision making, 286 democratic process, 288 economics degree, 278 e-government, 282, 285 electronic communication, 289 electronic-enabled public voice, 286 electronic information, 288 electronic media, 286 electronic records, 280, 284 electronic services, 294 e-mail, 289, 290, 295 forgiving technology, 296 front-office service, 282, 283 Google, 292 Google's cloud service, 290 Government 2, 287 Health and Social Care, 284 House business, 294 House of Lords, 288 ICT strategy, 289, 290 information management, 278 insurance company, 278 Internet information, 285 iPad, 288, 289, 296 IT data management, 279 management principle, 280 local government, 283 mainframe environment, 289 member-led activity, 287 messages, 289 Microsoft, 293 Microsoft's cloud service, 290 mobile electronic information, 284 mobile technology, 289 national organization, 284 network perimeters, 290 official government information, 285 on-the-job training, 281 organizational planning, 278 Parliamentary ICT, 277 project management, 279 public sector, 282 public transportation, 285 quango-type organizations, 283 representational democracy, 286 security, 290, 291 social care organization, 279 social care services, Essex, 278 social care systems, 284 social networking, 285 sovereignty, 291 sustainability and growth, 293 technical language, 294 technology skills, 281 transactional services, 285 transferability, 291 Web-based services, 285 Wikipedia, 291, 292 X-factor, 286 Minds for Sale, 20 Mitchell & Co, 333 MIT Media Labs, 149 Mobile computing, 62 Mobile technology, 234 Mooney, Mark, 133 artificial intelligence, 134 back-office legacy, 136 balancing standpoint, 145 BBC, 140 Bermuda Triangle, 135 BlackBerry shop, 142 Bureau of National Standards, 136 business model, 140 career spectrum, 144 cloud computing, 148 competitive intelligence and knowledge, 143 Connect, 141 customer-facing and product development, 135 customer-facing product space, 137 customer space and product development, 136 digital products development, 144 digital space and product, 146 educational and reference content, 139 educational products, 141 entrepreneur, 150 General Electric, 134 GradeGuru, 140 handheld devices, 142 hard-core technical standpoint, 146 hardware servers, 142 Houghton Mifflin, 134, 136 HTML, 138 industrial-strength product, 141 intellectual content, 148 Internet, 148 iPad, 138, 139, 142 iPhone, 142, 143 iTunes, 138 Klein, Joel, 147 learning management systems, 137 long-term production system, 141 Marine Corps, 134 McGraw-Hill Education, 133, 147 media development, 144 media space, 138, 142 mobile computing, 139 MOUSE, 150 online technology, 138 open-source capabilities, 142 Oracle quota-management system, 143 people's roles and responsibilities, 137 Phoenix, 149 product development, 149 publishing companies, 142 publishing systems, 137 Reed Elsevier, 133, 136 Salesforce.com, 144, 149 scalability testing, 145 senior business leaders, 146 social network, 148 soft discipline guidelines, 141 solar energy, 149 Strassmann, Paul, 135 technical skill set, 143, 144 testing systems integration, 145 The Shallows, 139 transactional systems, 142 trust and integrity, 145 TTS, QuickPro, and ACL, 144 Vivendi Universal, 134 War and Peace today, 139 Moore's law, 242 Morgan Stanley, 2, 3, 16 N NASA, 309, 333, 334 National Institute of Standards and Technology (NIST), 173 Naval Postgraduate School, 134 Netscape, 175 New Brunswick model, 282 News Corp., 147 New York Stock Exchange (NYSE), 87, 116, 223, 278 New York Times, 53 North American universities, 228 NSA/CIA software, 134 Nynex Mobile, 233 O Oil exploration office, 17 Open-source phone operating system, 20 Outlook, 15 P Pacer Software, 135 Paradigm shifts, 218, 220 Parks and Recreation Department, 126 PDP minicomputers, 212 Peopleware, 226 Personal computing, 235 Personal digital assistant (PDA), 238 Petri dish, 44 Phoenix, 211 Plauger, Bill, 2 Q Quants, 24 R Radio Shack, 51 Reed Elsevier, 133, 136 Reed, John, 335 Rubinow, Steve, 87 AdKnowledge, Inc., 87 agile development, 110 Agile Manifesto, 110 Archipelago Holdings Inc., 87 attributes, 108 capital market community, 91 cash/actual trading business, 88 channel marketing departments, 92 cloud computing, 97 CNBC, 89 collaborative technology, 95 collective intelligence, 95 communication skills, 102, 106 conference organizations, 99 consumer marketplace, 94 data center, 90 decision making, 105, 108 economy standpoint, 100 e-mail, 100 Fidelity Investments, 105 financial services, 92 IEEE, 101 innovative impression, 94 Internet, 98 iPad, 97 iPod device, 91 labor laws, 110 listening skills, 106 logical progression, 104 Mac, 96 mainframe, 104 management and leadership, 104, 105 market data system, 89 micro-second response time, 89 mobile applications, 94 multidisciplinary approach, 103 multimedia, 97 multi-national projects, 110 multiprocessing options, 99 network operating system, 103 NYSE Euronext, 87 open outside system, 88 parallel programming models, 99 personal satisfaction, 109 PR function, 106 proclaimed workaholic, 109 real estate business, 88 regulatory and security standpoint, 96 Rolodex, 94 Rubin, Howard, 99 server department, 97 software development, 89 sophisticated technology, 101 technology business, 88 technology integration, 91 trading engines, 90 typewriter ribbon, 94 virtualization, 98 Windows 7, 96 younger generation video games, 93 visual interfaces, 93 Rumsfeld, Donald, 222 S San Diego Fire Department, 224 Santa Clara University, 36 SAS programs, 131 Scott, Tony, 10, 33, 236 Android, 43 Apple Computer, 35 architectural flaw, 44 BASIC and Pascal, 35 Bristol-Myers Squibb, 33 Bunch, Rick (role model), 34 business groups, 42 COO, 39 Corporate Vice President, 33 Corvus disk drive, 36 CSC, 35 Defense department, 45 dogfooding, 37, 38 games and arcades, 35 General Motors, 33 IBM's role, 37 information systems management, 36 integrity factor, 40 Internet, 44 iPhone, 43 IT lifecycle management process, 37 leadership capability, 40 leisure studies, 34 macro-architectural threats, 44 Marriott's Great America, 35 math models, 36 Microsoft Corporation, 33, 36, 38, 41, 44, 46 Microsoft's operational enterprise risk management, 33 parks and recreation, 34 Petri dish, 44 playground leader, 42 product groups, 42 quality and business excellence team, 33 Santa Clara University, 36 Senior Vice President, 33 smartphone, 43 social computing, 38 Sun Microsystems, 36 theme park industry, 35 University of Illinois, 34 University of San Francisco, 36 value-added business, 33 Walt Disney Company, 33 Senior Leadership Technology and Product Marketing, 71 Shakespeare, 30 Shirky, Clay, 220 Sierra Ventures, 191 Silicon Valley companies, 68 Silicon Valley software factories, 323 Skype, 118 Smart Grid Advisory Committee, 177 Smartphones, 20, 27, 43, 54, 217, 238 Social care computer electronic record system, 279 Social computing, 38, 320 Social networking, 51, 53, 56, 58 Society trails technology, 21 SPSS programs, 131 Sridhara, Mittu, 71 Amazon, 76 American Airlines, 72 back-end computation and presentation, 80 banking, 77 B2B and B2C, 85 business/product departments, 82 business work context, 74 buzzword, 77 career aspiration, 73 career spans, 73 coders, 72 cognitive surplus, 79 competitive differentiation, 74 computing power, 78 contribution and energy, 85 convergence, 75 CPU cycles, 78 cross-channel digital business, 71 cultural and geographic implementation, 72 customer experience, 84, 85 customer profile, 76 data visualization, 79, 80 DDoS protection, 81 economies of scale, 77 elements of technology, 72 encryption, 82 end customer, 83 entertainment, 75 ERP system, 72 Facebook, 84 finance and accounting, 73 foster innovation and open culture, 81 friends/mentors/role models, 74 FSA, 76 gambling acts, 81 games, 79 gaming machines, 80 GDS, 72 global organization, 71 Google, 75, 84, 85 Group CIO, Ladbrokes PLC, 71 industry-standard technologies, 77 integrity and competence, 83 IT, 74, 82 KickOff app, 71 land-based casinos, 79 live streaming, 78 London Business School, 73 mobile computing, 78 multimedia, 84 new generation, 84 on-the-job training, 73 open-source computing, 79 opportunity, 80, 83 PCA-compliant, 81 personalization, 76 real-time systems, 74 re-evaluation, 81 reliability and availability, 77 security threats, 80 smart mobile device, 75 technology-intense customer, 85 top-line revenue, 74 trader apps, 82 true context, 73 underpinning business process, 76 virtualization, 78 Visa/MasterCard transactions, 78 Web 3.0 business, 76 web-emerging web channel, 76 Wikipedia, 79, 85 Word documents and e-mail, 82 work-life balance, 84 young body with high miles, 72 Zuckerberg, Mark, 73 Stead, Jerry, 214 Storefront engineering, 212 Strassmann, Paul, 228, 309 agile development, 340 Amazon EC2, 314 America information processors, 322 Annapolis, 340 AT&T, 332 backstabbing culture, 339 BlackBerry, 317 block houses, 319 CFO/CEO position, 337 CIM program, 309 Citibank, 337 Citicorp, 313, 339 cloud computing, 310, 311, 313 coding infrastructure, 341 communication infrastructure, 341 corporate information management, 329 Corporate Information Officer, 309 counterintelligence, 320 cyber-operations, 338 Dell server, 314 Department of Defense, 329, 332 Director of Defense Information, 309 employee-owned technology, 316 enterprise architecture, 316 exfiltration, 313 financial organizations, 320 firewalls and antiviruses, 312 General Foods, 309, 326–328 General Motors, 321, 329, 332 George Mason School of Information Technology, 309 Google apps, 314 government-supported activities, 326 Harvard Business School, 331 HR-related issues, 331 IBM manpower, 311 infiltration, 313 Internet, 316, 322 interoperability, 315, 317, 341 Kraft Foods Inc, 309 MacArthur's intelligence officer, 327 Machiavellian view, 327 mash-up, 316 military service, 331 NASA, 309, 333, 334 police department, economics, 312 powerpoint slides, 324 Radio Shack, 319 senior executive position, 334 service-oriented architecture, 316 Silicon Valley software factories, 323 social computing, 320 Strassmann's concentration camp, 318 structured methodologies, 342 U.S.

Handbook of Modeling High-Frequency Data in Finance
by Frederi G. Viens , Maria C. Mariani and Ionut Florescu
Published 20 Dec 2011

PROPOSITION 7.5 If X ∼ GHd (λ, χ , ψ, μ, , γ ) and Y = BX + b, where B ∈ Rk×d and b ∈ Rk , then Y ∼ GHk (λ, χ , ψ, Bμ + b, BB , Bγ ). Proof . i t  (BX +b) φY (t) = E(e it  b )=e  i t  (Bμ+b)  φX (B t) = e  H  t  BB t  − it Bγ . 2 This proposition shows that linear transformations of GH distributions remain in the class of GH distributions generated by the same GIG distribution N − (λ, χ , ψ), which is a useful property in portfolio management. COROLLARY 7.6 If B = ω = (ω1 , . . . , ωd ) and b = 0, then y = ω X is a one-dimensional GH distribution, and y ∼ GH1 (λ, χ , ψ, ω μ, ω ω, ω γ ). More specifically, the margins of X is X i ∼ GH1 (λ, χ , ψ, μi , ii , γi ). 170 CHAPTER 7 Risk Forecasting with Multiple Timescales This corollary shows that the method used in portfolio risk management based on multivariate normal distribution is also applicable to GH distribution.

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Moreover, when γ = 0, the skewed t distribution degenerates into the Student t distribution. As implied by its name, an inverse gamma r.v. is the inverse of a gamma r.v. Together with the mean–variance mixture definition, we can generate a skewed t r.v. accordingly. 171 7.2 The Skewed t Distributions ALGORITHM 7.8 1. 2. 3. 4. Simulation of the Skewed t Distribution. Generate Y from a Gamma( ν2 , ν2 ) distribution. Set W = Y −1 . By definition, W ∼ InverseGamma( ν2 , ν2 ). Generate a d-dimensional normal random vector Z ∼ Nd (0, Id ). Let √ X = μ + W γ + W AZ . Then X ∼ SkewT (ν, μ, , γ ). Other subfamilies of the GH distribution include Hyperbolic Distributions If λ = (d + 1)/2, we refer to the distribution as a d-dimensional hyperbolic distribution.

The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism
by Jeremy Rifkin
Published 31 Mar 2014

Silent Theft: The Private Plunder of Our Common Wealth. New York: Rutledge, 2003. ———. Viral Spiral. New York: The New Press, 2008. Bonpasse, Morrison. The Single Global Currency. Newcastle, ME: Single Global Currency Association, 2006. Borbely, Anne-Marie and Jan F. Kreider. Distributed Generation: The Power Paradigm for the New Millennium. Washington DC: CRC Press, 2001. Botsman, Rachel and Roo Rogers. What’s Mine Is Yours: The Rise of Collaborative Consumption. New York: HarperCollins, 2010. Boyle, James. Cultural Environmentalism and Beyond. San Francisco: Creative Commons, 2007.

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Race Against the Machine: How the Digital Revolution Is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy. Lexington, MA: Digital Frontier Press, 2011. Burger, Christoph and Jens Weinmann. The Decentralized Energy Revolution. New York: Palgrave Macmillan, 2013. Carr, Nicholas. The Big Switch. New York: W.W. Norton, 2009. Chambers, Ann. Distributed Generation. Tulsa: PennWell Corporation, 2001. Chandler Jr., Alfred D. The Visible Hand: The Managerial Revolution in American Business. Cambridge: The Belknap Press of Harvard University Press, 1977. Chesbrough, Henry. Open Innovation. Boston: Harvard Business School Press, 2006. Christman, John.

Anarchy State and Utopia
by Robert Nozick
Published 15 Mar 1974

Almost every suggested principle of distributive justice is patterned: to each according to his moral merit, or needs, or marginal product, or how hard he tries, or the weighted sum of the foregoing, and so on. The principle of entitlement we have sketched is not patterned.am There is no one natural dimension or weighted sum or combination of a small number of natural dimensions that yields the distributions generated in accordance with the principle of entitlement. The set of holdings that results when some persons receive their marginal products, others win at gambling, others receive a share of their mate’s income, others receive gifts from foundations, others receive interest on loans, others receive gifts from admirers, others receive returns on investment, others make for themselves much of what they have, others find things, and so on, will not be patterned.

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Holdings ought not to be distributed according to natural assets. But differences in natural assets might be correlated with other differences that are not arbitrary from a moral point of view and that are clearly of some possible moral relevance to distributional questions. For example, Hayek argued that under capitalism distribution generally is in accordance with perceived service to others. Since differences in natural assets will produce differences in ability to serve others, there will be some correlation of differences in distribution with differences in natural assets. The principle of the system is not distribution in accordance with natural assets; but differences in natural assets will lead to differences in holdings under a system whose principle is distribution according to perceived service to others.

The New Map: Energy, Climate, and the Clash of Nations
by Daniel Yergin
Published 14 Sep 2020

They are shifting from traditional “central” generation, based on coal and gas and nuclear power plants, to “distributed and intermittent” generation based on wind farms and solar panels that are spread across the landscape. But “distributed” systems create new challenges, especially in terms of grid stability and reliability, which is a fundamental mission of utilities. “With the advancement of distributed generation, with the monitoring of two-way flows on the system, with managing circuit overload potentials, more technology is going to have to be put into storage and control mechanisms,” says Christopher Crane, CEO of the U.S. utility Exelon and chairman of the Edison Electric Institute.6 * * * — How fast will the transition be, and what will it look like on the other side?

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Some companies are already players in wind; and some, long accustomed to building and managing large complex offshore oil and gas platforms, are now entering the offshore wind business. If the future is increasing electrification, to what degree will oil and gas companies be moving into electric power? Some already are. Financial returns will be a question. Power and renewable projects—“lower-carbon generation and distribution”—generally operate in highly-regulated markets and deliver lower rates of return than those traditionally of oil and gas projects. How will they square the circle with demands for returns from investors—which have to meet the retirement and pension needs of their fund-holders—and yet deliver an increasingly “green” portfolio for activist shareholders and millennial investors interested in “impact”?

Economics Rules: The Rights and Wrongs of the Dismal Science
by Dani Rodrik
Published 12 Oct 2015

Acemoglu, Daron, 206 advertising, prisoners’ dilemma and, 14–15 Africa, Washington Consensus and, 162 agriculture: subsidies in, 149, 194 subsistence vs. modern, 75, 88 Airbus, 15 airline industry, deregulation of, 168 Akerlof, George, 68, 69n Algan, Yann, 79n, 200n Allen, Danielle, xiv American Economic Review (AER), 30–31 American Political Science Review (APSR), 30–31 Angrist, Joshua, 108 antelopes, 35n antipoverty programs, 3–4 cash grants vs. subsidies in, 4 antitrust law, 161 Argentina, 166 arguments, mathematics and, 35n Arrow, Kenneth, 31, 49–51 Ash, Michael, 77 Asia, economic growth and, 163–64, 166 asset bubbles, 152–58 asymmetric information, 68–69, 70, 71 Auctions: Theory and Practice (Klemperer), 36n “Auctions and Bidding: A Primer” (Milgrom), 36n auction theory, 36, 168 automobiles, effect of sales tax and demand on, 180–81 balanced budgets, 171 Bangladesh, 57–58, 123 Bank of England, 197 banks, banking, 1n, 2 computational models and, 38 credit rationing in, 64–65 globalization and, 165–66 Great Recession and, 152–59 insurance in, 155 regulation of, 155, 158–59 shadow sector in, 153 bargaining, 124–25, 143 Battle of Bretton Woods, The: John Maynard Keynes, Harry Dexter White, and the Making of a New World Order (Steil), 1n–2n bed nets, randomized testing and, 106, 204 behavioral economics, 69–71, 104–7, 202–4 Berlin, Isaiah, 175 Bernanke, Ben, 134–35 Bertrand competition, 68 Bhagwati, Jagdish, 182n–83n big data, 38–39, 40 Bloomberg, Michael, 4 Boeing, 15 Böhm-Bawerk, Eugen von, 119 Bordo, Michael D., 127n Borges, Jorge Luis, 43–44, 86 Boston University, 3 Boughton, James M., 1n Boulding, Kenneth, 11 bounded rationality, 203 Bowles, Samuel, 71n Brazil: antipoverty programs of, 4 globalization and, 166 Bretton Woods Conference (1944), 1–2 Britain, Great, property rights and, 98 bubbles, 152–58 business cycles, 125–37 balanced budgets and, 171 capital flow in, 127 classical economics and, 126–27, 129, 137 inflation in, 126–27, 133, 135, 137 new classical models and, 130–34, 136–37 butterfly effect, 39 California, University of: at Berkeley, 107, 136, 147 at Los Angeles, 139 Cameron, David, 109 capacity utilization rates, 130 capital, neoclassical distribution theory and, 122, 124 capital flow: in business cycles, 127 economic growth and, 17–18, 114, 164–67 globalization and, 164–67 growth diagnostics and, 90 speculation and, 2 capitalism, 118–24, 127, 144, 205, 207 carbon, emissions quotas vs. taxes in reduction of, 188–90, 191–92 Card, David, 57 Carlyle, Thomas, 118 carpooling, 192, 193–94 cartels, 95 Cartwright, Nancy, 20, 22n, 29 cash grants, 4, 55, 105–6 Cassidy, John, 157n Central Bank of India, 154 Chang, Ha-Joon, 11 chaos theory, butterfly effect and, 39 Chicago, University of, 131, 152 Chicago Board of Trade, 55 Chile, antipoverty programs and, 4 China, People’s Republic of, 156, 163, 164 cigarette industry, taxation and, 27–28 Clark, John Bates, 119 “Classical Gold Standard, The: Some Lessons for Today” (Bordo), 127n classical unemployment, 126 climate change, 188–90, 191–92 climate modeling, 38, 40 Cochrane, John, 131 coffee, 179, 185 Colander, David, 85 collective bargaining, 124–25, 143 Colombia, educational vouchers in, 24 colonialism, developmental economics and, 206–7 “Colonial Origins of Comparative Development, The” (Acemoglu, Robinson, and Johnson), 206–7 Columbia University, 2, 108 commitment, in game theory, 33 comparative advantage, 52–55, 58n, 59–60, 139, 170 compensation for risk models, 110 competition, critical assumptions in, 28–29 complementarities, 42 computable general equilibrium (CGE) models, 41 computational models, 38, 41 computers, model complexity and, 38 Comte, Auguste, 81 conditional cash transfer (CCT) programs, 4, 105–6 congestion pricing, 2–3 Constitution, U.S., 187 construction industry, Great Recession and, 156 consumers, consumption, 119, 129, 130, 132, 136, 167 cross-price elasticity in, 180–81 consumer’s utility, 119 contextual truths, 20, 174 contingency, 25, 145, 173–74, 185 contracts, 88, 98, 161, 205 coordination models, 16–17, 42, 200 corn futures, 55 corruption, 87, 89, 91 costs, behavioral economics and, 70 Cotterman, Nancy, xiv Cournot, Antoine-Augustin, 13n Cournot competition, 68 credibility, in game theory, 33 “Credible Worlds, Capacities and Mechanisms” (Sugden), 172n credit rating agencies, 155 credit rationing, 64–65 critical assumptions, 18, 26–29, 94–98, 150–51, 180, 183–84, 202 cross-price elasticity, 180–81 Cuba, 57 currency: appreciation of, 60, 167 depreciation of, 153 economic growth and, 163–64, 167 current account deficits, 153 Curry, Brendan, xv Dahl, Gordon B., 151n Darwin, Charles, 113 Davis, Donald, 108 day care, 71, 190–91 Debreu, Gerard, 49–51 debt, national, 153 decision trees, 89–90, 90 DeLong, Brad, 136 democracy, social sciences and, 205 deposit insurance, 155 depreciation, currency, 153 Depression, Great, 2, 128, 153 deregulation, 143, 155, 158–59, 162, 168 derivatives, 153, 155 deterrence, in game theory, 33 development economics, 75–76, 86–93, 90, 159–67, 169, 201, 202 colonial settlement and, 206–7 institutions and, 98, 161, 202, 205–7 reform fatigue and, 88 diagnostic analysis, 86–93, 90, 97, 110–11 Dijkgraaf, Robbert, xiv “Dirtying White: Why Does Benn Steil’s History of Bretton Woods Distort the Ideas of Harry Dexter White?” (Boughton), 1n distribution, general theory of, 121 distribution, neoclassical theory of, 122, 124 Dixit, Avinash, xv, 61 Doan, pınar, xv Doing Growth Diagnostics in Practice (Hausmann, Klinger, and Wagner), 111n dollar, depreciation of, 153 dual economy models, 88 Duflo, Esther, 107 duopolies, 13n, 68 Dutch disease syndrome, 60, 61, 73, 100 East Anglia, University of, 172n Econometrica, 36 econometrics, 133 economic growth, 86–93, 90, 97, 110–11, 147–49 capital flow in, 17–18, 114, 164–67 currency in, 163–64, 167 public spending in, 76–78, 114 Washington Consensus and, 159–67, 169 economics: ambition, humility and, 208–11 antipoverty programs and, 3–4 behavioral factors in, 69–71, 104–7, 202–4 business cycles in, 125–37 contingent explanations of social life in, xiii critical assumptions in, 18, 26–29, 94–98, 150–51, 180, 183–84, 202 critics and, 177–211 definitions of, 7 developing economies and, see development economics efficiency in, xiii, 14, 21, 34, 48, 50, 51, 67, 98, 125, 147, 148, 150, 156–58, 161, 165, 170, 192–95, 196 errors of commission in, 159–67 errors of omission in, 152–59 field experiments in, 23–24, 105–8, 173, 202–5 game theory and, 5, 14–15, 33, 36, 61–62, 68, 103–4, 133 government policies and, 75–76, 87, 88, 147–48, 149, 160–61, 171 “hedgehog” vs.

MacroWikinomics: Rebooting Business and the World
by Don Tapscott and Anthony D. Williams
Published 28 Sep 2010

“We need a new paradigm,” said Gordon. “We can’t go on borrowing the same underlying assumptions and design traditions that underpin the industrial age model of power generation.” Naturally, the utility companies—who have little to gain from distributed generation—are not particularly enthralled with the Frasers’ arguments.19 Taken to its logical conclusion, distributed generation would unleash massive disruption and potentially make redundant a large part of what power utilities do today. Rather than selling power—a majority of which would be produced locally by homeowners and businesses—the core of the industry would shift to selling green energy installations and energy services like smart grid apps that help households and businesses save money.

Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages
by Carlota Pérez
Published 1 Jan 2002

The other two structural tensions stem from the same basic cause: the whole frenzy phenomenon is, at bottom, a huge process of income redistribution in favor of those directly or indirectly involved in the casino, which funds the massive process of creative destruction in the economy. That regressive distribution generates a double vicious cycle: one is economic, expressed in the market; the other is social, expressed in political terms. Both get worse as the bubble increases. The economic vicious cycle sets in as the reversal of the virtuous cycle that created the casino prosperity. The concentration of income in the prosperous fringe of the population works wonderfully for providing a high investment rate, accompanied by a high dynamic demand for the early products of the revolution and many others that complement the new lifestyle.

A World of Three Zeros: The New Economics of Zero Poverty, Zero Unemployment, and Zero Carbon Emissions
by Muhammad Yunus
Published 25 Sep 2017

The poor women employed as members of the Grameen Marketing Network sell products that include mobile telephone handsets and accessories, solar panels and mini solar energy systems, chemically treated mosquito nets to reduce the incidence of malaria and other infectious diseases, and energy-efficient light fixtures and bulbs. With a market reach of over 1.5 million rural households, Grameen Distribution generates grassroots employment for many thousands of village women, increasing their family incomes by an average of US$37 per month. In a country where (for example) the minimum monthly wage in the huge garment industry is as low as US$68, that’s a significant boost to a family’s efforts to work their way out of poverty.8 One more social business example out of the many others I could name is the Grameen Caledonian College of Nursing, which opened its doors to students in March 2010.

Does Capitalism Have a Future?
by Immanuel Wallerstein , Randall Collins , Michael Mann , Georgi Derluguian , Craig Calhoun , Stephen Hoye and Audible Studios
Published 15 Nov 2013

Developing countries will likely struggle for a reformed and more egalitarian capitalism just as Westerners did in the first half of the 20th century. Some will be more successful than others, as was the case in the West. China faces the severest problems now. The benefits of its phenomenal growth are very unequally distributed, generating major protest movements. Revolutionary turbulence is certainly possible there, but if successful it would likely bring in more capitalism and perhaps an imperfect democracy, as happened in Russia. America also faces severe challenges since its economy is overloaded with military and health spending, its polity is corrupted and dysfunctional, and the ideology of its conservatives has turned against science and social science.

Empirical Market Microstructure: The Institutions, Economics and Econometrics of Securities Trading
by Joel Hasbrouck
Published 4 Jan 2007

These findings, if correct, impose substantive restrictions on the sorts of models that can sensibly be estimated. Can safely we ignore this evidence? After all, why should one be concerned about convergence failures in infinite samples? The answer is that whatever one’s beliefs about the properties of the distribution generating the data, the existence of extreme values in finite samples is an irrefutable fact leading to many practical consequences. Sample estimates may be dominated by a few extreme values. Increasing sample size does not increase precision as fast we’d expect. Estimated parameters are sensitive to model specification.

The Grand Scuttle
by Dan Van der Vat

Things improved with practice, but every now and again the crews had to sit in the dark wrapped in their greatcoats and blankets as the engineers sought to eke out the coal stocks to the next delivery. To conserve oil, the destroyers were subjected to ‘lights out’ at 11.30 p.m. when their diesels were turned off. The same report said that the British system of distributing general supplies round the fleet was working well and the Royal Navy had managed to find a water-tender, something it had never needed to do before. This was the Flying Kestrel from Liverpool, which took eight days to make a complete round of the interned ships, and which was on hand at the very moment the ships were scuttled.

How to Form Your Own California Corporation
by Anthony Mancuso
Published 2 Jan 1977

Contributions and accumu­ lated earnings under such plans are not taxed until they are distributed to the employee. This is advan­tageous because employees generally will be in a lower tax bracket at retirement age, and the funds, while they are held in trust, can be invested and allowed to accumulate with no chapter 4 | corporate taxation | 89 tax due on investment income or gains prior to their distribution. Generally speaking, there are two basic types of pension plans: defined-contribution and defined-benefit plans (there are some hybrid types as well). A defined-contribution plan guarantees a specified yearly contribution of no more than 25% of an employee’s annual salary to each employee. A defined-benefit plan guarantees a specified benefit upon retirement, which can be as large as 100% of an employee’s annual pay during his or her highest-paid years.

Augmented: Life in the Smart Lane
by Brett King
Published 5 May 2016

Ahmad Chatila, SunEdison’s CEO, says that improvements in RE storage are critical for smart energy management based on solar generation. “The most important technology we can develop right now is storage.” Ahmad Chatila, CEO of SunEdison Alternative smart energy systems might include: • distributed generation networks (resilient against terrorist attack and natural disasters) • fuel cell generation/storage systems • small module thorium reactors (shipping container-sized reactors that generate 300 megawatt hours (MWh), or enough for 45,000 homes on demand) • retooling skyscrapers and government buildings with transparent solar cells (otherwise known as PV window panes) • coastal cities with surge walls that also generate energy from tidal forces Smart Health Care As discussed in chapter 5, recent technology developments in mobile health monitoring systems, also called mHealth, incorporate wearable sensors and algorithms that will track a patient’s vital signs and condition.

Blood and Oil: Mohammed Bin Salman's Ruthless Quest for Global Power
by Bradley Hope and Justin Scheck
Published 14 Sep 2020

Each had dozens of staff waiting to satisfy any princely whim. The problem was that Salman had spent, and not saved or invested, much of his share of oil profits, and he hadn’t started lucrative side businesses like other, more entrepreneurial princes. He didn’t control a license to sell Mercedes-Benz cars or distribute General Electric products—typical means princes used to grow their income. While Salman had achieved great political might, he had relatively little wealth by Al Saud standards. His close family had some investments in companies and real estate but lived what amounted to a lavish hand-to-mouth existence, dependent on payouts from the king and treasury.

Capital in the Twenty-First Century
by Thomas Piketty
Published 10 Mar 2014

Two points need to be clarified at once. First, we find this regularity in all countries in all periods for which data are available, without exception, and the magnitude of the phenomenon is always quite striking. To give a preliminary idea of the order of magnitude in question, the upper 10 percent of the labor income distribution generally receives 25–30 percent of total labor income, whereas the top 10 percent of the capital income distribution always owns more than 50 percent of all wealth (and in some societies as much as 90 percent). Even more strikingly, perhaps, the bottom 50 percent of the wage distribution always receives a significant share of total labor income (generally between one-quarter and one-third, or approximately as much as the top 10 percent), whereas the bottom 50 percent of the wealth distribution owns nothing at all, or almost nothing (always less than 10 percent and generally less than 5 percent of total wealth, or one-tenth as much as the wealthiest 10 percent).

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Such a chaste approach is all the more regrettable in that it inevitably feeds the wildest fantasies and tends to discredit official statistics and statisticians rather than calm social tensions. Conversely, interdecile ratios are sometimes quite high for largely artificial reasons. Take the distribution of capital ownership, for example: the bottom 50 percent of the distribution generally own next to nothing. Depending on how small fortunes are measured—for example, whether or not durable goods and debts are counted—one can come up with apparently quite different evaluations of exactly where the tenth percentile of the wealth hierarchy lies: for the same underlying social reality, one might put it at 100 euros, 1,000 euros, or even 10,000 euros, which in the end isn’t all that different but can lead to very different interdecile ratios, depending on the country and the period, even though the bottom half of the wealth distribution owns less than 5 percent of total wealth.

Nerds on Wall Street: Math, Machines and Wired Markets
by David J. Leinweber
Published 31 Dec 2008

Source: GridPoint (www.gridpoint.com). 334 Nerds on Wall Str eet GridPoint explains how its simple blue box on the wall addresses all the key issues in our electricity future: The platform applies information technology to the electric grid to enable distributed energy resources to perform the same as central-station generation. During peak periods, utilities efficiently balance supply and demand by discharging stored power from distributed generation assets or reducing customers’ non-essential loads through demand response programs. Additionally, utilities effectively optimize baseload generation assets and relieve stress on transmission and distribution systems. The platform enables utilities to deploy proven technologies, (e.g., load control devices and advanced batteries) while creating a practical path for integrating new technologies (e.g., plug-in hybrid electric vehicles [PHEVs] and fuel cells).

Business Lessons From a Radical Industrialist
by Ray C. Anderson
Published 28 Mar 2011

The courses that many teach offer no sustainable solutions for the world their graduates will find. They are still part of the problem. Aspiring mechanical engineers still learn everything about internal combustion engines and not enough about fuel cells. Budding electrical engineers are still taught all about coal-fired central power plants, and not enough about distributed generating capacity from clean, renewable resources like the wind and the sun (or the local, methane-rich garbage dump!). Ceramics engineers still learn the traditional, abusive, heat-beat-treat methods that, quite literally, go back a thousand years or more. Meanwhile, they overlook the abalone’s natural nanostructural methods that use self-organized proteins and minerals dissolved in cold seawater to produce a ceramic product—mother-of-pearl nacre—that is superior to any man-made ceramic.

Doing Data Science: Straight Talk From the Frontline
by Cathy O'Neil and Rachel Schutt
Published 8 Oct 2013

priors in, Adding Priors timestamped event data exercise, Exercise: GetGlue and Timestamped Event Data–Exercise: Financial Data financial models, Financial Modeling–A Baby Model causality and, In-Sample, Out-of-Sample, and Causality–In-Sample, Out-of-Sample, and Causality data preparation for, Preparing Financial Data–Preparing Financial Data exponential downweighting, Exponential Downweighting feedback loop, The Financial Modeling Feedback Loop–The Financial Modeling Feedback Loop in-sample data, In-Sample, Out-of-Sample, and Causality–In-Sample, Out-of-Sample, and Causality log returns and, Log Returns out-of-sample data, In-Sample, Out-of-Sample, and Causality–In-Sample, Out-of-Sample, and Causality S&P index example, Example: The S&P Index volatility measurements in, Working out a Volatility Measurement–Working out a Volatility Measurement finite differences, In practice Firebug extension, Scraping the Web: APIs and Other Tools Firefox, Scraping the Web: APIs and Other Tools Flickr, Scraping the Web: APIs and Other Tools FOIA requests, A Bit of History on Data Journalism Foreign Affairs (magazine), Datafication forward selection, Selecting an algorithm Fourier coefficients, Productionizing machine learning models fraud, data visualization and, The Risk Challenge–Detecting suspicious activity using machine learning detecting with machine learning, Detecting suspicious activity using machine learning–Detecting suspicious activity using machine learning performance estimation, The Trouble with Performance Estimation–Challenges in features and learning freeze rates, Data Visualization at Square Fruchterman-Reingold algorithm, Morningside Analytics functions, Fitting a model knn(), Choosing k likelihood, Estimating α and β loss, Adding in modeling assumptions about the errors penalty, A Baby Model fundamental problem of causal inference, The Rubin Causal Model G garbage in, garbage out scenarios, William Cukierski Gaussian distribution, Probability distributions Gaussian mixture model, Linear Regression Geller, Nancy, The Current Landscape (with a Little History) Gelman, Andrew, Modeling general functional form, Probability distributions generating data, The Life of a Chief Data Scientist generative processes, Machine Learning Algorithms geo-based location data, Populations and Samples of Big Data geographic information systems (GIS), What Is Data Science, Redux? geometric mean, Theorem: The resulting latent features will be uncorrelated GetGlue, Kyle Teague and GetGlue–Kyle Teague and GetGlue, Exercise: Build Your Own Recommendation System GetGlue exercise, Exercise: GetGlue and Timestamped Event Data–Exercise: Financial Data Giraph, Pregel GitHub, Back to Josh: Workflow Gmail, Why Won’t Linear Regression Work for Filtering Spam?

Chokepoint Capitalism
by Rebecca Giblin and Cory Doctorow
Published 26 Sep 2022

It’s still unclear at what rate they plan to pay—the meager sales royalty rate adopted by Sony, Warner’s 25 percent, or the 50 percent licensing rate that most fairly reflects their risk and reward. But when the shares are sold, UMG artists will receive at least some money in their pockets. A 2019 Deutsche Bank report estimates that streaming is the most profitable form of music distribution, generating an 18 percent margin, compared to 13 percent on downloads and just 11 percent on physical. But as we’ve seen, those profits aren’t being evenly enjoyed. Daniel Glass, president and founder of Glassnote Records, says, “There’s very little middle- and lower-class in recording. That world has dried up.”

J.K. Lasser's Your Income Tax 2016: For Preparing Your 2015 Tax Return
by J. K. Lasser Institute
Published 19 Oct 2015

If the amortization payment were increased to $50,000, only $30,000 of the distribution would be tax free ($80,000 – $50,000). Real estate investment trusts (REITs). The tax treatment of real estate investment trusts resembles that of open-end mutual funds. Distributions generally are reported to the investors on Form 1099-DIV as dividend income. However, distributions generally do not qualify for the reduced tax rate on qualified dividends (4.1). A distribution qualifies for the reduced rate only to the extent it represents previously taxed undistributed income or qualifying dividends received by the REIT (from stock investments) that are passed through to the investors.

…

If you want to change IRA investments in the 12-month period ending July 14, 2016, you can do so by authorizing a direct transfer between IRA trustees, as direct transfers are not limited. In addition, a conversion of a traditional IRA to a Roth IRA is not affected by the rollover limitation and can be made at any time; see above. Transition rule disregards certain 2014 distributions. Generally, the one-rollover-per-year limitation means that you will be taxed on an IRA distribution if you made an IRA-to-IRA rollover in the preceding 12 months. However, the IRS provided a transition rule that allows rollovers of certain 2014 distributions to be disregarded, thereby allowing rollover treatment for a 2015 distribution that otherwise would be prohibited by the limitation.

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The balance of qualifying expenses after subtracting tax-free educational assistance and credit-related expenses is the beneficiary’s adjusted qualified educational expenses. The Example below shows how the taxable portion of the distribution is determined when the total distribution exceeds the adjusted qualified education expenses. Additional tax on taxable distributions. Generally, a taxable distribution is subject to a 10% additional tax, which is figured on Form 5329. However, the 10% additional tax does not apply to distributions that are: (1) made to a beneficiary (or to the estate of the designated beneficiary) on or after the death of the designated beneficiary, (2) made because the designated beneficiary is disabled, (3) taxable because the designated beneficiary received a tax-free scholarship or educational assistance allowance that equals or exceeds the distribution, or (4) taxable only because the qualified ESA education expenses were reduced by expenses used in figuring an American Opportunity or Lifetime Learning credit.

Statistics hacks
by Bruce Frey
Published 9 May 2006

Create Your Own Standardized Score For fun, you can create your own standardized score distribution with any mean and standard deviation you wish. Don't like your SAT score of 350? Transform it into a score within a distribution of your choosing. Imagine, for example, that you'd prefer a distribution with a mean of 752,365 and a standard deviation of 216,456 (and who wouldn't?). Let's call this distribution the Frey Score Distribution. Generalizing the T score formula, you could transform your SAT score of 350 into a Frey score. Remember, you have to start with the z score for an SAT score of 350: and then transform it into a Frey score: Now, doesn't a score of 427,681 sound better than a score of 350? Because you know the mean of the Frey distribution, the interpretation of both scores is the same; they are still below average, and they are still 11/2 standard deviations below the mean.

The Future of Technology
by Tom Standage
Published 31 Aug 2005

However, a complete return to that model would be folly, for it would rob both the grid and micropower plants of the chance to sell power when the other is in distress. Rather, the grid will be transformed into a digital network capable of handling complex, multi-directional flows of power. Micropower and megapower will then work together. abb foresees the emergence of “microgrids” made up of all sorts of distributed generators, including fuel cells (which combine hydrogen and oxygen to produce electricity cleanly), wind and solar power. The University of California at Irvine is developing one, as are some firms in Germany. “Virtual utilities” would then aggregate the micropower from various sources in real time – and sell it to the grid.

The Missing Billionaires: A Guide to Better Financial Decisions
by Victor Haghani and James White
Published 27 Aug 2023

This result is actually somewhat surprising, as advocates of Kelly betting often suggest investors use half‐Kelly to account for uncertainty in expected returns, but this example suggests that may not be an entirely consistent response to this kind of uncertainty. The Shape of Uncertainty Uncertainty about the return distribution of financial assets goes well beyond not knowing the expected return and expected standard deviation. We will often be uncertain regarding the distribution's general shape and character—for example whether the distribution is skewed, fat‐tailed, or involves large jumps in price. As we saw in Chapter 16 in discussing options, the shape of the distribution can have a big impact on whether or not options will increase an investor's Expected Utility. However, in some important cases—such as deciding how much to invest in equities in typical market conditions—the shape of the distribution can have surprisingly little effect on the optimal decision.

The Art of Computer Programming: Fundamental Algorithms
by Donald E. Knuth
Published 1 Jan 1974

[HM23] When the probability that some quantity has the value k is e~^(/j,k/k\), it is said to have the Poisson distribution with mean \x. a) What is the generating function for this set of probabilities? b) What are the values of the semi-invariants? c) Show that asn-^oo the Poisson distribution with mean np approaches the normal distribution in the sense of exercise 13. 16. [M25] Suppose X is a random variable whose values are a mixture of the proba- probability distributions generated by gi(z), 52B), ..., gr(z), in the sense that it uses gk{z) with probability pk, where pi + p2 + ¦ ¦ ¦ + pr — 1- What is the generating function for XI Express the mean and variance of X in terms of the means and variances of g\, ?2, ..., gr. > 17. [M27] Let f(z) and g(z) be generating functions that represent probability dis- distributions. a) Show that h(z) = g(f(z)) is also a generating function representing a probability distribution. b) Interpret the significance of h(z) in terms of f(z) and g(z).

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(a) Prove that Pr(X < nr) < {r^e7)", when 0 < r < 1; Pr(X > nr) < (r^e^Y. when r > 1. (b) Express the right-hand sides of these estimates in convenient form when r ~ 1. (c) Show that if r is sufficiently large we have Pr(X > /j,r) < 2~Mr. 23. [HM23] Estimate the tail probabilities for a random variable that has the negative binomial distribution generated by (q — pz)~n, where q = p + 1. *1.2.11. Asymptotic Representations We often want to know a quantity approximately, instead of exactly, in order to compare it to another. For example, Stirling's approximation to n! is a useful representation of this type, when n is large, and we have also made use of the fact that Hn w Inn + 7.

CTOs at Work
by Scott Donaldson , Stanley Siegel and Gary Donaldson
Published 13 Jan 2012

So now I know that I've got 100 megawatts of storage in a given area. I've got 30 megawatts of thermal energy through ice that I froze that evening and it is ready to melt as needed so I don't have to use an air conditioning compressor. Then I've got a certain amount of distributed resources through distributed generation sources. That may be solar, it may be wind, it may be backup generation, and it may be some other form of biomass. Now … we can't control the sun. We can't control the wind—yet. But what we can do through tight coupling of the other resources is make a more predictable supply and demand curve, because the goal is predictability.

Red-Blooded Risk: The Secret History of Wall Street
by Aaron Brown and Eric Kim
Published 10 Oct 2011

People who think like me call ourselves data analysts, and we like nonparametric or distribution-free methods. We use a lot of rough tools, like jackknifes, bootstraps, cross-tabulations, and visualizations. We like to let the data speak to us instead of trying to fit it in a preconceived model, and we like simple methods that do not require strong assumptions about either the distribution generating the data or our prior knowledge. So on my own time, for my dissertation, I was trying a third approach to the conditional heteroskedasticity problem. A popular technique among data analysts is resampling. To predict the future, you string together randomly selected days from the past—with replacement, meaning you can pick the same past day more than once.

The End of Alchemy: Money, Banking and the Future of the Global Economy
by Mervyn King
Published 3 Mar 2016

In addition, there is an active over-the-counter market in bilateral transactions. 8 Several theoretical papers have tried to express coordination failures in terms of an abstract game-theoretic description of an economy; for example, Cooper and John (1988) and, more recently, Angeletos et al. (2014). Such abstraction is not necessary to understand the coordination problem, even if its consequences are fundamental. 9 Krugman (2011). 10 Other path-breaking contributions were made by, among others, the American economists Tom Sargent and Neil Wallace. 11 Random shifts in the distributions generating shocks in a neoclassical model, termed ‘extrinsic uncertainty’ by Hendry and Mizon (2014), are similar to radical uncertainty. 12 Such models were described as ‘New Keynesian’ despite the fact that Keynes’s view of recessions did not depend on the slow adjustment of wages and prices to external shocks and that the essence of Keynes, namely radical uncertainty and the resulting prisoner’s dilemma, was absent from them. 13 They are sometimes described as ‘dynamic stochastic general equilibrium’ (DSGE) models, and have become the basis for much modern macroeconomics.

Inventors at Work: The Minds and Motivation Behind Modern Inventions
by Brett Stern
Published 14 Oct 2012

Merfeld: I think the whole stationary grid space is exciting, underdeveloped, and in the early percolation stages of evolving. There’s a lot of excitement about this space, and I think it could be an opportunity like the hybridization of transportation to get better fuel economy. Applying that same concept around how we do power generation for electricity is going to be pretty exciting. Whether it’s distributed generation, whether it’s allowing the increased penetration of renewables, whether it changes in the way we actually think, whether it changes the models that we can use to envision how you make and sell electricity, I think energy storage has the promise to change that whole space. Stern: This technology is fairly macro.

Commodity Trading Advisors: Risk, Performance Analysis, and Selection
by Greg N. Gregoriou , Vassilios Karavas , François-Serge Lhabitant and Fabrice Douglas Rouah
Published 23 Sep 2004

If managed futures can provide downside protection, we would expect the average monthly downside return to be smaller than that observed for our initial stock/bond portfolio. Once again, we build a blended portfolio of 55 percent U.S. stocks, 35 percent U.S. treasury bonds, and 10 percent CTA strategy. We then develop a frequency distribution of monthly returns over the period 1990 to 2000. In Table 11.3 we present the results from the return distribution generated by this CTA-blended portfolio for each CTA strategy. For example, for CTA agriculture, the average downside return is −1.81 percent. This is an improvement of 26 basis points over the average downside return observed with the stock/bond portfolio. The number of downside months with CTA agriculture managed futures added to the portfolio increased by one month to 43.

The Wizard of Menlo Park: How Thomas Alva Edison Invented the Modern World
by Randall E. Stross
Published 13 Mar 2007

Edison Electric’s backers, who had waited patiently for four years and were now eager to see a return on their investment, urged Edison to promote the product line that customers were most interested in, the on-site plants. Edison sought vindication of his belief that centralized power generation was superior to distributed generation, but he acknowledged that here, too, he needed help to make a success of the central-station business. He appointed his own personal secretary, twenty-three-year-old Samuel Insull, to take charge of the campaign. Insull had immigrated from England two years before to work for Edison, and his employer had come to view him as indispensable.

Mastering Private Equity
by Zeisberger, Claudia,Prahl, Michael,White, Bowen , Michael Prahl and Bowen White
Published 15 Jun 2017

The bottom-up valuation method calculates the price of an LP secondary by discounting the projected future cash flows of the fund in question. The discount rate in this scenario is based on the gross return generated from fund cash flows that the secondary buyer expects to achieve. The cash flows include expected distributions from existing portfolio investments, drawdowns for future primary and follow-on investments, and distributions generated from these future investments. Cash flow expectations for existing portfolio companies are estimated by examining the stage of the investment, its expected growth rate and investment needs, and exit valuation expectations, among other metrics. Projecting drawdowns and returns for future investments is less straightforward: estimating the timing and size of cash flows can be based only on due diligence calls with fund managers and the GP’s past performance record.

Seven Crashes: The Economic Crises That Shaped Globalization
by Harold James
Published 15 Jan 2023

The 1970s brought home to self-satisfied American automobile producers that their cars were not as efficient as ones made in Japan, a country that in the 1960s had been ridiculed as making knock-off products that were crude, colorful, and cheap. And the 2020s? The Covid crisis exposed deep fissures and tensions in many societies. There were questions about who suffered and how burdens were distributed. Generating an effective vaccine response required tackling profound inequalities and differences of outlook, which were directly reflected in differing rates of vaccine uptake. The challenge highlighted the attractions for the United States of previously derided European social security systems. But it also shed a new light on the potential offered by use of personalized data on smartphones in China to combat public health crises.

From Counterculture to Cyberculture: Stewart Brand, the Whole Earth Network, and the Rise of Digital Utopianism
by Fred Turner
Published 31 Aug 2006

Even as the Free Speech Movement and the New Left explicitly confronted military, industrial, and academic institutions, the bohemian artists of cold war Manhattan and San Francisco, and later the hippies of Haight-Ashbury and the youthful back-to-the-landers, in fact embraced the technocentric optimism, the information theories, and the collaborative work style of the research world. Fully in keeping with the scientific ethos of the era, young members of the New Communalist wing of the counterculture, along with many in the New Left, imagined themselves as part of a massive, geographically distributed, generational experiment. The world was their laboratory; in it they could play both scientist and subject, exploring their minds and their bodies, their relationships to one another, and the nature of politics, commerce, community, and the state. Small-scale technologies would serve them in this work.

The Future of the Internet: And How to Stop It
by Jonathan Zittrain
Published 27 May 2009

• There must be a way for an individual to correct or amend a record of identifiable information about him. • Any organization creating, maintaining, using, or disseminating records of identifiable personal data must assure the reliability of the data for their intended use and must take precautions to prevent misuse of the data.101 These recommendations present a tall order for distributed, generative systems. It may seem clear that the existence of personal data record-keeping systems ought not to be kept secret, but this issue was easier to address in 1973, when such systems were typically large consumer credit databases or government dossiers about citizens, which could more readily be disclosed and advertised by the relevant parties.

Beginning R: The Statistical Programming Language
by Mark Gardener
Published 13 Jun 2012

You can use the command in two ways: you can see what the current settings are and you can also alter these settings. If you type the command without any instructions (that is, just a pair of parentheses) you see the current settings: > RNGkind() [1] "Mersenne-Twister" "Inversion" Two items are listed. The first is the standard number generator and the second is the one used for normal distribution generation. To alter these, use the kind = and normal.kind = instructions along with a text string giving the algorithm you require; this can be abbreviated. The following example alters the algorithms and then resets them: > RNGkind(kind = 'Super', normal.kind = 'Box') > RNGkind() [1] "Super-Duper" "Box-Muller" > RNGkind('default') > RNGkind() [1] "Mersenne-Twister" "Box-Muller" > RNGkind('default', 'default') > RNGkind() [1] "Mersenne-Twister" "Inversion" Here you first alter both kinds of algorithm.

The Men Who United the States: America's Explorers, Inventors, Eccentrics and Mavericks, and the Creation of One Nation, Indivisible
by Simon Winchester
Published 14 Oct 2013

Imaginary houses, designed to look like those in lower Manhattan, were also staked out, and they were lighted, too, and this whole unreal New York City was connected to an array of batteries with feeder cables (which took the power to the streets), mains wires (which took it into the houses), and service wires (which went to the individual house lamps). Edison’s basic plan for electric distribution—generator, feeder, mains, service—remains today the standard model for all distribution everywhere. Once he threw the switches, his display burst into a frenzy of glitter. It immediately and mightily impressed the city’s Blue Book visitors. The Vanderbilts, prominent from their railroading fortune, were the first to back Edison’s efforts.

A World Beneath the Sands: The Golden Age of Egyptology
by Toby Wilkinson
Published 19 Oct 2020

Moreover, despite the long-standing animosity between the agents of British and French consuls – one of Salt’s employees lived just down the hill from Wilkinson’s house, while an Italian working for Drovetti had a house nearby; the two men argued frequently over the precise boundaries of their adjoining concessions, and distributed generous bribes to local officials to advance their respective cases – Wilkinson remained neutral. He seems not to have been especially bothered by the removal of the Luxor obelisk to Paris. He happily left his workmen unsupervised, allowing them to steal artefacts and sell them to collectors; yet he took great pains in his own investigations not to damage the delicate reliefs inside the tomb of Seti I.

J.K. Lasser's Your Income Tax
by J K Lasser Institute
Published 30 Oct 2012

If the amortization payment were increased to $50,000, only $30,000 of the distribution would be tax free ($80,000 − $50,000). Real estate investment trusts (REITs). The tax treatment of real estate investment trusts resembles that of open-end mutual funds. Distributions generally are reported to the investors on Form 1099-DIV as dividend income. However, distributions generally do not qualify for the reduced tax rate on qualified dividends (4.1). A distribution qualifies for the reduced rate only to the extent it represents previously taxed undistributed income or qualifying dividends received by the REIT (from stock investments) that are passed through to the investors.

…

The balance of qualifying expenses after subtracting tax-free educational assistance and credit-related expenses is the beneficiary’s adjusted qualified educational expenses. The Example below shows how the taxable portion of the distribution is determined when the total distribution exceeds the adjusted qualified education expenses. Additional tax on taxable distributions. Generally, a taxable distribution is subject to a 10% additional tax, which is figured on Form 5329. However, the 10% additional tax does not apply to distributions that are: (1) made to a beneficiary (or to the estate of the designated beneficiary) on or after the death of the designated beneficiary, (2) made because the designated beneficiary is disabled, (3) taxable because the designated beneficiary received a tax-free scholarship or educational assistance allowance that equals or exceeds the distribution, or (4) taxable only because the qualified ESA education expenses were reduced by expenses used in figuring an American Opportunity or Lifetime Learning credit.

J.K. Lasser's Your Income Tax 2014
by J. K. Lasser
Published 5 Oct 2013

If the amortization payment were increased to $50,000, only $30,000 of the distribution would be tax free ($80,000 – $50,000). Real estate investment trusts (REITs) The tax treatment of real estate investment trusts resembles that of open-end mutual funds. Distributions generally are reported to the investors on Form 1099-DIV as dividend income. However, distributions generally do not qualify for the reduced tax rate on qualified dividends (4.1). A distribution qualifies for the reduced rate only to the extent it represents previously taxed undistributed income or qualifying dividends received by the REIT (from stock investments) that are passed through to the investors.

…

The balance of qualifying expenses after subtracting tax-free educational assistance and credit-related expenses is the beneficiary’s adjusted qualified educational expenses. The Example below shows how the taxable portion of the distribution is determined when the total distribution exceeds the adjusted qualified education expenses. Additional tax on taxable distributions Generally, a taxable distribution is subject to a 10% additional tax, which is figured on Form 5329. However, the 10% additional tax does not apply to distributions that are: (1) made to a beneficiary (or to the estate of the designated beneficiary) on or after the death of the designated beneficiary, (2) made because the designated beneficiary is disabled, (3) taxable because the designated beneficiary received a tax-free scholarship or educational assistance allowance that equals or exceeds the distribution, or (4) taxable only because the qualified ESA education expenses were reduced by expenses used in figuring an American Opportunity or Lifetime Learning credit.

Trade Your Way to Financial Freedom
by van K. Tharp
Published 1 Jan 1998

equity curve The value of your account over time, illustrated in a graph. exit That part of your trading system that tells you how or when to exit the market. expectancy How much you can expect to make on the average over many trades. Expectancy is best stated in terms of how much you can make per dollar you risk. Expectancy is the mean R of an R-multiple distribution generated by a trading system. expectunity A term used in this book to express expectancy multiplied by opportunity. For example, a trading system that has an expectancy of 0.6R and produces 100 trades per year will have an expectunity of 60R. false positive Something that gives a prediction that then fails to happen.

The Debian Administrator's Handbook, Debian Wheezy From Discovery to Mastery
by Raphaal Hertzog and Roland Mas
Published 24 Dec 2013

The extract below uses the wheezy-proposed-updates alias which is both more explicit and more consistent since squeeze-proposed-updates also exists (for the Oldstable updates): deb http://ftp.debian.org/debian wheezy-proposed-updates main contrib non-free 6.1.2.4. Stable Backports The stable-backports repository hosts “package backports”. The term refers to a package of some recent software which has been recompiled for an older distribution, generally for Stable. When the distribution becomes a little dated, numerous software projects have released new versions that are not integrated into the current Stable (which is only modified to address the most critical problems, such as security problems). Since the Testing and Unstable distributions can be more risky, package maintainers sometimes offer recompilations of recent software applications for Stable, which has the advantage to limit potential instability to a small number of chosen packages

Imagining India
by Nandan Nilekani
Published 25 Nov 2008

His DC “micropower” systems failed because the technology was unreliable and expensive. Edison had to watch a couple of his DC plants literally go up in flames before he gave up on the idea. dk I have chaired two committees on IT in the power sector, and the second one recommended having such “Smart Grids” that can deal with distributed generation and multiple renewable sources.

Piracy : The Intellectual Property Wars from Gutenberg to Gates
by Adrian Johns
Published 5 Jan 2010

In Cardiff, for example, he was accused of trespassing, which led to an open debate in court about whether a stall was “as sacred as an Englishman’s private house.” “That one spot is,” it was affirmed. People with real addresses – shopkeepers, coffeehouse men, publicans, and so on – were an altogether more serious matter. Their fixed premises meant they could often act as local centers of distribution. Generally, hawkers would be supplied from some such house, pub, or other outlet, with the actual warehouse being a small distance away down a back street. Two examples stand out as notable. One was the Manchester shop of a young man called by the press “Himie Cohen,” where Preston found thirty hawkers collecting piracies to sell (some of them escaped out of a window).

J.K. Lasser's Your Income Tax 2022: For Preparing Your 2021 Tax Return
by J. K. Lasser Institute
Published 21 Dec 2021

If the amortization payment were increased to $50,000, only $30,000 of the distribution would be tax free ($80,000 – $50,000). Real estate investment trusts (REITs). The tax treatment of real estate investment trusts resembles that of open-end mutual funds. Distributions generally are reported to the investors on Form 1099-DIV as dividend income. However, distributions generally do not qualify for the reduced tax rate on qualified dividends (4.2) but may be eligible for the qualified business income (QBI) deduction (31.16). A distribution qualifies for the reduced rate (but not the QBI deduction) only to the extent it represents previously taxed undistributed income or qualifying dividends received by the REIT (from stock investments) that are passed through to the investors.

…

The balance of qualifying expenses after subtracting tax-free educational assistance and credit-related expenses is the beneficiary’s AQEE. The Example below shows how the taxable portion of the distribution is determined when the total distribution exceeds the AQEE. Additional tax on taxable distributions. Generally, a taxable distribution is subject to a 10% additional tax, which is figured on Form 5329. However, the 10% additional tax does not apply to distributions that are: (1) made to a beneficiary (or to the estate of the designated beneficiary) on or after the death of the designated beneficiary, (2) made because the designated beneficiary is disabled, (3) taxable because the designated beneficiary received a tax-free scholarship or educational assistance allowance that equals or exceeds the distribution, or (4) taxable only because the qualified ESA education expenses were reduced by expenses used in figuring an American opportunity or lifetime learning credit.

The Taste of War: World War Two and the Battle for Food
by Lizzie Collingham
Published 1 Jan 2011

In Essen bread and potatoes made up 90 per cent of what most people ate and the industrial towns were once more hard hit by an unsatisfactory potato harvest in the summer.170 The Ministry of Food began to make plans to distribute swedes and turnips, and Italian rice and lentils were brought in to eke out the supplies of staple foods.171 The basic meat and fat ration had to be cut yet again in May.172 Even though military rations were cut by 20 per cent, the army was too large a burden on the system. All this was exacerbated by the 7 million foreign workers in the Reich and the fact that local officials began to distribute generous rations to the homeless in the bombed-out cities.173 In the spring of 1943 Sybil Bannister, an Englishwoman married to a German gynaecologist, discovered how serious the food situation in the towns was in comparison with the comparatively plentiful food supply in the countryside. Sybil spent the first years of the war living in Bromberg, a town in the annexed part of Poland.

The Empathic Civilization: The Race to Global Consciousness in a World in Crisis
by Jeremy Rifkin
Published 31 Dec 2009

The smart intergrid will not only give end users more power over their energy choices, but it also creates new energy efficiencies in the distribution of electricity. The intergrid makes possible a broad redistribution of power. Today’s centralized, top-down flow of energy becomes increasingly obsolete. In the new era, businesses, municipalities, and homeowners become the producers as well as the consumers of their own energy—what is referred to as “distributed generation.” The distributed smart grid also provides the essential infrastructure for making the transition from the oil-powered internal combustion engine to electric and hydrogen fuel-cell plug-in vehicles. Electric plug-in and hydrogen-powered fuel-cell vehicles are also “power stations on wheels” with a generating capacity of twenty or more kilowatts.

The Stack: On Software and Sovereignty
by Benjamin H. Bratton
Published 19 Feb 2016

Berardi, Neuro Totalitarianism (Los Angeles: Semiotext(e), 2014). 28.  Logistics here refers to both the expert mobilization of things, but also the aestheticized image of mobility in action. For the latter, logistics is a technical imaginary for the world in choreographic motion, an image that in turn becomes a technique for organizing the world as a distributed, generalized complex of distributed, integrated interfaces. 29.  A possible methodological framework: Interface design is less about the design of a thing than of a condition of transference (that could become a thing) and can take at least three main forms. First-order interface design produces the conditions of interassemblage between people, things, or places—making it good, smart, fast, flexible, sustainable, and so on.

Global Catastrophic Risks
by Nick Bostrom and Milan M. Cirkovic
Published 2 Jul 2008

But if it happened quickly, large numbers ofpeople might find themselves economically superfluous. This would tend to facilitate their oppression as well as reducing their motivation to produce and innovate. In an extreme scenario, the resulting loss of human potential might be considered catastrophic. Another potential problem raised by distributed general-purpose manu­ facturing is a variety of new forms of crime. Even something as simple as a portable diamond saw capable of quickly cutting through concrete could facilitate breaking and entering. Medical devices might be used for illegal psychoactive purposes. Sensors could be used to violate privacy or gather passwords or other information.

Enlightenment Now: The Case for Reason, Science, Humanism, and Progress
by Steven Pinker
Published 13 Feb 2018

This includes mathematical analyses in which scientists plot the distribution of events in the past (like wars or cyberattacks) and show they fall into a power-law distribution, one with “fat” or “thick” tails, in which extreme events are highly improbable but not astronomically improbable.7 The math is of little help in calibrating the risk, because the scattershot data along the tail of the distribution generally misbehave, deviating from a smooth curve and making estimation impossible. All we know is that very bad things can happen. That takes us back to subjective readouts, which tend to be inflated by the Availability and Negativity biases and by the gravitas market (chapter 4).8 Those who sow fear about a dreadful prophecy may be seen as serious and responsible, while those who are measured are seen as complacent and naïve.

Data Mining: Concepts and Techniques: Concepts and Techniques
by Jiawei Han , Micheline Kamber and Jian Pei
Published 21 Jun 2011

Heuristically, we can add constraints on the distribution that is generating outliers. For example, it is reasonable to assume that this distribution has a larger variance if the outliers are distributed in a larger area. Technically, we can assign , where k is a user-specified parameter and σ is the standard deviation of the normal distribution generating the normal data. Again, the EM algorithm can be used to learn the parameters. 12.3.2. Nonparametric Methods In nonparametric methods for outlier detection, the model of “normal data” is learned from the input data, rather than assuming one a priori. Nonparametric methods often make fewer assumptions about the data, and thus can be applicable in more scenarios.

1967: Israel, the War, and the Year That Transformed the Middle East
by Tom Segev
Published 2 Jan 2007

Only three cities had not yet been captured—Nablus, Hebron, and Jericho—but they were encircled. The air force had shot down twenty-three more planes, about half Egyptian and half Iraqi, and had lost five of its own. The number of Israeli dead had reached 460: 225 in the south and 235 on the West Bank.46 The commander of Southern Command distributed General Order No. 2 among his troops: “Never before have so few pilots destroyed so many aircraft in such a short time.” He urged them to keep up the good work: “Follow the enemy and strike him down, hit him again and again, until he is defeated by the sword of the fighters of the Southern Command,” he wrote.47 CHAPTER 14 DAY THREE 1.

Before the Storm: Barry Goldwater and the Unmaking of the American Consensus
by Rick Perlstein
Published 17 Mar 2009

Evetts Haley, A Texan Looks at Lyndon: A Study in Illegitimate Power (Canyon, Tex.: Palo Duro Press, 1964). For LBJ: A Political Biography, see GRR, October 31, 1964; and Diamond, Roads to Dominion, 153. 478 At rallies the books were handed out: author interview with Ann Sullivan. For Spanish, LP versions, Virginia sales, and distribution generally, see O‘Brien field reports, October 1, 1964, October 2, 1964, October 6, 1964, and October 20, 1964, LBJWHA: Wilson, Box 3/Memos to the President—O’Brien Trips. Hear also LBJ and Houston Harte, August 31, 1964, LBJT, 6408.42/12. 500,000 were sent out by the Walter Knott-led group Citizens for Constructive Action; see Lisa McGirr, “Suburban Warriors: Grass-Roots Conservatism in the 1960s” (Ph.D. diss., Columbia University, 1995), 166. 478 “Your letter to the President”: September 16, 1964, draft for Moyers letter, LBJWHAM53.

Valuation: Measuring and Managing the Value of Companies
by Tim Koller , McKinsey , Company Inc. , Marc Goedhart , David Wessels , Barbara Schwimmer and Franziska Manoury
Published 16 Aug 2015

In contrast, General Mills substantially reduced costs in Pillsbury’s purchasing, manufacturing, and distribution, because the two companies’ operations duplicated significant costs. On the revenue side, General Mills boosted Pillsbury’s revenues by introducing Pillsbury products to schools in the United States where General Mills already had a strong presence. And the synergies worked both ways: for instance, Pillsbury’s refrigerated trucks were used to distribute General Mills’ new line of refrigerated meals. Pillsbury represented value in at least two ways at the time of the sale: its value to General Mills and its value to Diageo. For General Mills to consider the deal attractive, Pillsbury’s worth under General Mills’ ownership had to be greater than the $10.4 billion purchase price.

Thomas Cromwell: A Life
by Diarmaid MacCulloch
Published 26 Sep 2018

This time Husee dared to point out that the bride was the Queen’s sister.13 The venue was 9 miles from London, at Cromwell’s country house at Mortlake, a hasty journey for the Lord Privy Seal from accompanying the King on his summer progress further west in Surrey, at Sunninghill and Guildford. Richard Cromwell handed over fifty pounds to Gregory as a present from his father, then the Lord Privy Seal himself distributed generous gifts of cash round the household.14 Apart from any political discretion about the wedding, the plague was currently raging in London, and every public ceremony was low key. A rural location like Mortlake would not only alleviate the wedding party’s anxieties, but soothe the King’s perennial obsession with outsiders bringing infection to the Court, an anxiety heightened at the time by the Queen’s advanced pregnancy.

The C++ Programming Language
by Bjarne Stroustrup
Published 2 Jan 1986

. (∗2.5) Plot points in a square output area. The coordinate pairs for the points should be generated by U Urraanndd(N N), where N is the number of pixels on a side of the output area. What does the output tell you about the distribution of numbers generated by U Urraanndd? 15. (∗2) Implement a Normal distribution generator, N Nrraanndd. The C++ Programming Language, Third Edition by Bjarne Stroustrup. Copyright ©1997 by AT&T. Published by Addison Wesley Longman, Inc. ISBN 0-201-88954-4. All rights reserved. 688 Numerics Chapter 22 The C++ Programming Language, Third Edition by Bjarne Stroustrup. Copyright ©1997 by AT&T.

Stigum's Money Market, 4E
by Marcia Stigum and Anthony Crescenzi
Published 9 Feb 2007

Specifically, under the Fed’s ruling, a bank was prohibited from extending any credit to its securities affiliate, except intraday credit extended in connection with the clearing of U.S. government securities; also, under the Fed’s 1987 ruling, a bank holding company was permitted to lend to its securities subsidiary, but such loans had to be overcollateralized or deducted from the capital of the holding company. Where banks had an advantage over most securities dealers was in international distribution. Generally, the biggest of the banks had operations in more foreign financial centers and had been there for longer than the biggest of the U.S. nonbank dealers. Profitability No Barrier to Reform Much of the time when U.S. banks were fighting for the right to underwrite an expanded menu of securities, their profits were rising sharply.