Bayesian statistics

Everything Is Predictable: How Bayesian Statistics Explain Our World

by Tom Chivers  · 6 May 2024  · 283pp  · 102,484 words

, it came as a kind of a shock to discover that in most statistical conferences you had to fight for your right to work within Bayesian statistics to a mainly unsympathetic audience, with no real time left to go into the details of your work,” Bernardo went on. Grieve remembers something similar

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Full Monty Carlo’ was before the smartphone era, so no recordings exist.”111 (“Who would want to see a video of six male professors of Bayesian statistics taking their clothes off in front of a screaming crowd in a Spanish nightclub?”112 he went on to ask, in my view entirely misjudging

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all got a bit hairy and life preservers were thrown. “If we’d all have drowned,” he said, “that would have knocked the development of Bayesian statistics on the head rather.” “I have a sweatshirt from the third Valencia conference,” Grieve says cheerfully, “saying ‘Bayesians have more fun.’ ” In the decades since

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of trouble.”115 Meanwhile Dennis Lindley wasn’t exactly pouring oil on troubled water, telling a conference in 1975 that “the only good statistics is Bayesian statistics. [Bayes] is not just another technique to be added to our repertoire alongside, for example, multivariate analysis: it is the only method that can produce

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seem to be the case. Jens Koed Madsen, a cognitive psychologist at the London School of Economics, told me that he uses both frequentist and Bayesian statistics, depending on the question he’s trying to answer. Sophie Carr, a Bayesian statistician and founder of a consulting firm literally called Bays, says that

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needle much,” he says. “It might give you a hint of being convinced, but it doesn’t drown out your prior skepticism. That’s what Bayesian statistics gives the scientist, a vehicle for skepticism, a way to say, ‘I don’t believe this theory.’ “It’s a perverse incentive, for scientists to

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of as the arch-frequentist, says that “often frequentist approaches are best, but sometimes you do have enough prior information to say we can use Bayesian statistics, and in those situations it has clear advantages. That’s the nuanced position, but you’re not going to write a book saying that.” Cassie

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you want to approach your decision-making.”48 She also points out, probably rightly, that during her graduate studies at Duke University—“which is to Bayesian statistics approximately what the Vatican is to Catholicism”—the loudest voices shouting about how great Bayesianism is weren’t the professors but the students, mainly because

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to grasp. Sophie Carr, the statistician who runs the consultancy firm called Bays, is surprisingly nondogmatic about it as well. “I talk about frequentist and Bayesian statistics like rugby,” she says. The two codes of rugby—league and union—have subtly different rules, and fans of the two different disciplines are loudly

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Theory That Would Not Die. 108. José M. Bernardo, “The Valencia Story: Some Details on the Origin and Development of the Valencia International Meetings on Bayesian Statistics,” ISBA Newsletter, December 1999, https://www.uv.es/bernardo/ValenciaStory.pdf. 109. Ibid. 110. P. R. Freeman and A. O’Hagan, “Thomas Bayes’s Army

Prophecy: Prediction, Power, and the Fight for the Future, from Ancient Oracles to AI

by Carissa Véliz  · 21 Apr 2026  · 503pp  · 129,255 words

probability as an expression of our degree of certainty about something and not as a property out there in the world. The core idea behind Bayesian statistics is that we should update our probabilities based on new evidence, which allows us to incorporate prior knowledge and continuously refine our understanding as more

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, confidence intervals, and p values. The fundamental assumption is that there is an objective truth that can be approached through repeated sampling and statistical analyses. Bayesian statistics views probability as a measure of subjective belief or uncertainty. The Bayesian goes around collecting more and more data, updating her beliefs again and again

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, it took us away from causes, and away from religious thinking. Life is funny that way; even the destiny of ideas is unpredictable. Frequentist and Bayesian statistics have opposite methods. Bayes’s theorem asks how likely a hypothesis is to be true, given the data you’ve seen. Frequentists ask, if your

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our coin will land heads, we are saying something both about the world and about our confidence in our belief. One of the strengths of Bayesian statistics is that you can consider all the data you have access to. If you do a study with frequentist statistics, you start from scratch; every

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the case, the strength of Bayesian thinking, taking into account prior data and updating it, is also its weakness: the choice of prior distribution. In Bayesian statistics, the selection of prior probabilities can significantly influence results, potentially introducing biases. In 2023, a group of nine “expert forecasters” estimated a 30.5 percent

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of England, 165 bank runs, 129–30 Bard, 101 Barrera, Alonso, 289 Bastet, 295 Bayes, Thomas, 35–36, 50, 110 thought experiment of, 106–7 Bayesian statistics, 35–36, 40, 106–7, 109–12, 143, 162, 163 Bear Sterns, 256 Beauvoir, Simone de, 220 Beck, Ulrich, 180, 190 Beckman, Preston, 236 bell

Artificial Intelligence: A Modern Approach

by Stuart Russell and Peter Norvig  · 14 Jul 2019  · 2,466pp  · 668,761 words

). The papers collected by Gilks et al. (1996) cover both theory and applications of MCMC. Since the mid-1990s, MCMC has become the workhorse of Bayesian statistics and statistical computation in many other disciplines including physics and biology. The Handbook of Markov Chain Monte Carlo (Brooks et al., 2011) covers many aspects

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brings together work from the fields of statistics and pattern recognition, so the story has been told many times in many ways. Good texts on Bayesian statistics include those by DeGroot (1970), Berger (1985), and Gelman et al. (1995). Bishop (2007), Hastie et al. (2009), Barber (2012), and Murphy (2012) provide excellent

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Systems, includes many Bayesian learning papers, as does the annual conference on Artificial Intelligence and Statistics. Specifically Bayesian venues include the Valencia International Meetings on Bayesian Statistics and the journal Bayesian Analysis. 1Statistically sophisticated readers will recognize this scenario as a variant of the urn-and-ball setup. We find urns and

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). Using the SIR algorithm to simulate posterior distributions. In Bernardo, J. M., de Groot, M. H., Lindley, D. V., and Smith, A. F. M. (Eds.), Bayesian Statistics 3. Oxford University Press. Rubinstein, A. (1982). Perfect equilibrium in a bargaining model. Econometrica, 50, 97–109. Rubinstein, A. (2003). Economics and psychology? The case

The Elements of Statistical Learning (Springer Series in Statistics)

by Trevor Hastie, Robert Tibshirani and Jerome Friedman  · 25 Aug 2009  · 764pp  · 261,694 words

rates for projection pursuit regression and neural network training, Annals of Statistics 20: 608–613. Jordan, M. (2004). Graphical models, Statistical Science (Special Issue on Bayesian Statistics) 19: 140–155. Jordan, M. and Jacobs, R. (1994). Hierachical mixtures of experts and the EM algorithm, Neural Computation 6: 181–214. Kalbfleisch, J. and

The Art of Statistics: How to Learn From Data

by David Spiegelhalter  · 2 Sep 2019  · 404pp  · 92,713 words

the standard deviation of the population distribution divided by the square root of the sample size. * We shall see in Chapter 12 that practitioners of Bayesian statistics are happy using probabilities for epistemic uncertainty about parameters. * Strictly speaking, a 95% confidence interval does not mean there is a 95% probability that this

The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant From Two Centuries of Controversy

by Sharon Bertsch McGrayne  · 16 May 2011  · 561pp  · 120,899 words

probable cause. And he discovered the long-sought grail of probability, what future mathematicians would call the probability of causes, the principle of inverse probability, Bayesian statistics, or simply Bayes’ rule. Given the revered status of his work today, it is also important to recognize what Bayes did not do. He did

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probability theory. As the first since Laplace to apply formal Bayesian theory to a variety of important scientific problems, Jeffreys became the founder of modern Bayesian statistics. Statistically, the lines were drawn. Jeffreys and Fisher, two otherwise cordial Cambridge professors, embarked on a two-year debate in the Royal Society’s Proceedings

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, taboo since the nineteenth century. As Savage explained, when he wrote the book he was “not yet a personalistic Bayesian.” He thought he came to Bayesian statistics “seriously only through recognition of the likelihood principle; and it took me a year or two to make the transition.”21 According to the likelihood

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only combative one, Lindley defended Bayes’ rule like a fearless terrier or a devil’s advocate. In return, he was tolerated almost as comic relief. “Bayesian statistics is not a branch of statistics,” he argued. “It is a way of looking at the whole of statistics.” Lindley became known as a modern

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Bayesians and frequentists. RAND gradually weaned itself from air force funding by diversifying into social welfare research. The world can be thankful that Madansky’s Bayesian statistics forced the military to tighten safety measures. A number of false alerts suggestive of Soviet nuclear attacks were identified correctly before SAC could launch a

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and Schlaifer’s classic book for advanced statisticians, Applied Statistical Decision Theory, was published in 1961. Its careful, detailed analytical methods set the direction of Bayesian statistics for the next two decades. Today it sits on almost every decision analyst’s bookshelf. When Pratt joined Raiffa and Schlaifer to write Introduction to

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. It followed works by Jeffreys, Savage, and Raiffa and Schlaifer. Of all these works, only Mosteller and Wallace’s had dared treat real issues with Bayesian statistics and modern computers. Mosteller had thought about The Federalist papers for 23 years and worked on them for ten. It would long remain the largest

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motivation, Tukey’s secrecy edict played a major role in the history of Bayes’ rule. As Wallace observed, “It’s important to the development of Bayesian statistics that a lot was under wraps.”31 Tukey’s censorship of his polling methods for NBC News, like the highly classified status of Bayesian cryptography

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checked to see what Laplace had done before tackling an applied problem, turned off many colleagues with his Bayesian fervor. Dennis Lindley was slowly building Bayesian statistics departments in the United Kingdom but quit administration in 1977 to do solo research. Jack Good moved from the super-secret coding and decoding agencies

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, so most researchers were still limited to “toy” problems and trivialities. Models were not complex enough. The title of a meeting held in 1982, “Practical Bayesian Statistics,” was a laughable oxymoron. One of Lindley’s students, A. Philip Dawid of University College London, organized the session but admitted that “Bayesian computation of

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was their number one issue. Almost overnight, coal mines got safer. “It was a Eureka moment,” Raftery said. “It was quite a thrill. And without Bayesian statistics, it would have been much harder to do a test of this hypothesis.”5 Frequency-based statistics worked well when one hypothesis was a special

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frenzy of excitement. Problems that had been nightmares cracked open as easily as eggs for an omelet. A dozen years earlier the conference title “Practical Bayesian Statistics” had been a joke. But after 1990 Bayesian statisticians could study data sets in genomics or climatology and make models far bigger than physicists could

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wanted to use graphical models for simulations. Once again, Clayton was an important influence. Spiegelhalter unveiled his free, off-the-shelf BUGS program (short for Bayesian Statistics Using Gibbs Sampling) in 1991. BUGS caused the biggest single jump in Bayesian popularity. It is still the most popular software for Bayesian analyses, and

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data, which “generally covered only the past two decades, a period of euphoria . . . [instead of] historic periods of stress.”5 But did Greenspan actually employ Bayesian statistics to quantify empirical economic data? Or were Bayesian concepts about uncertainty only a handy metaphor? Former Reserve Board governor Alan S. Blinder of Princeton thought

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, James C., and Heacox, Linda. (1999) Credibility theory: The cornerstone of actuarial science. North American Actuarial Journal (3:2) 1–8. Jewell, William S. (2004) Bayesian statistics. Encyclopedia of Actuarial Science. Wiley. 153–66. Kahn, PM. (1975) Credibility: Theory and Applications. Academic Press. Klugman SA, Panjer HH, Willmot GE. (1998) Loss Models

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JC. (1974) Insurance credibility theory and Bayesian estimation. In Credibility: Theory and Applications, ed. PM Kahn, 249–70. Miller Robert B. (1989) Actuarial applications of Bayesian statistics. In Bayesian Analysis in Econometrics and Statistics: Essays in Honor of Harold Jeffreys, ed. Arnold Zellner. Robert E. Krieger. Morris C, Van Slyke L. (1978

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Statistician (18) 313–26. ———. (1980) L. J. Savage—his work in probability and statistics. Annals of Statistics (8) 1–24. ———. (1983) Theory and practice of Bayesian statistics. The Statistician (32) 1–11. ———. (1986) Savage revisited: Comment. Statistical Science (1) 486–88. ———. (1990) Good’s work in probability, statistics and the philosophy of

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R. A. Fisher. Clarendon Press. DeGroot, MH. (1986c) A conversation with Charles Stein. Statistical Science (1) 454–62. Edwards W, Lindman H, Savage LJ. (1963) Bayesian statistical inference for psychological research. Psychological Research (70:3) 193–242. Efron, Bradley. (1977) Stein’s paradox in statistics. Scientific American (236) 119–27. ———. (1978) Controversies

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. Decision Analysis Society Newsletter (11:2). Bilstein, Roger E. (1977) Development of aircraft engines and fuels. Technology and Culture 18) 117–18. Birnberg JG. (1964) Bayesian statistics: A review. Journal of Accounting Research (2) 108–16. Fienberg, Stephen E. (2008) The early statistical years: 1947–1967. A conversation with Howard Raiffa. Statistical

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Diaconis. Statistical Science (1:3) 319–34. Diaconis P, Efron B. (1983) Computer-intensive methods in statistics. Scientific American (248) 116–30. Diaconis, Persi. (1985) Bayesian statistics as honest work. Proceedings of the Berkeley Conference in Honor of Jerzy Neyman and Jack Kiefer (1), eds., Lucien M. Le Cam and Richard A

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. Olshen. Wadsworth. Diaconis P, Holmes S. (1996) Are there still things to do in Bayesian statistics? Erkenntnis (45) 145–58. Diaconis P. (1998) A place for philosophy? The rise of modeling in statistical science. Quarterly of Applied Mathematics (56:4) 797

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spike train decoding applied to position prediction from ensemble firing patterns of rat hippocampal place cells. Journal of Neuroscience (18) 7411–25. Campbell, Gregory. (2009) Bayesian statistics at the FDA: The trailblazing experience with medical devices. Emerging Issues in Clinical Trials, Rutgers Biostatistics Day, April 3, 2009. http://www.stat.rutgers.edu

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. IEEE Internet Computing (7:1) 76–80. Ludlum, Robert. (2005) The Ambler Warning. St. Martin’s. O’Hagan A, Luce BR. (2003) A Primer on Bayesian Statistics in Health Economics and Outcomes Research. MEDTAP International. Pearl, Judea. (1988) Probabilistic Reasoning in Intelligence Systems: Networks of Plausible Inference. Morgan Kaufman Publishers. Pouget A

The Book of Why: The New Science of Cause and Effect

by Judea Pearl and Dana Mackenzie  · 1 Mar 2018

that “most of” the tools of statistics strive for complete objectivity. There is one important exception to this rule, though. A branch of statistics called Bayesian statistics has achieved growing popularity over the last fifty years or so. Once considered almost anathema, it has now gone completely mainstream, and you can attend

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the other hand, if we already suspected the coin was weighted, we would conclude more willingly that the nine heads provided serious evidence of bias. Bayesian statistics give us an objective way of combining the observed evidence with our prior knowledge (or subjective belief) to obtain a revised belief and hence a

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. Macmillan, London, UK. Goldberger, A. (1972). Structural equation models in the social sciences. Econometrica: Journal of the Econometric Society 40: 979–1001. Lindley, D. (1987). Bayesian Statistics: A Review. CBMS-NSF Regional Conference Series in Applied Mathematics (Book 2). Society for Industrial and Applied Mathematics, Philadelphia, PA. McGrayne, S. B. (2011). The

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–120 junctions in, 113–116 in machine learning, 125 parent nodes in, 117 probability in, 358–359 probability tables in, 128–129 SCMs versus, 284 Bayesian statistics, 89–91 Bayes’s rule, 101–104, 196 BCSC. See Breast Cancer Surveillance Consortium belief, 101–102 belief propagation, 112–113, 128 Berkeley admission paradox

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, 18 causality and, 18, 66, 190 confounders in, 138–139, 141–142 methods of, 31, 180–181 objectivity and, 89 skepticism in, 178 See also Bayesian statistics Stigler, Stephen, 63, 71, 147 Stott, Peter, 292–294 strong AI, 3, 11 causal reasoning of, 20–21 counterfactuals for, 269 free will and, 358

The Art of Statistics: Learning From Data

by David Spiegelhalter  · 14 Oct 2019  · 442pp  · 94,734 words

standard deviation of the population distribution divided by the square root of the sample size. 4 We shall see in Chapter 12 that practitioners of Bayesian statistics are happy using probabilities for epistemic uncertainty about parameters. 5 Strictly speaking, a 95% confidence interval does not mean there is a 95% probability that

Rationality: From AI to Zombies

by Eliezer Yudkowsky  · 11 Mar 2015  · 1,737pp  · 491,616 words

’s what the Bayesians say. But frequentists don’t believe that.” And I said, astounded: “How can there possibly be such a thing as non-Bayesian statistics?” That was when I discovered that I was of the type called “Bayesian.” As far as I can tell, I was born that way. My

The Signal and the Noise: Why So Many Predictions Fail-But Some Don't

by Nate Silver  · 31 Aug 2012  · 829pp  · 186,976 words

sure way for him to know that. The proper way for the player to estimate his odds of being a winner, instead, is to apply Bayesian statistics,31 where he revises his belief about how good he really is, on the basis of both his results and his prior expectations. If the

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/%E9%AB%98%E7%B5%B1%E5%A0%B1%E5%91%8A.pdf. 62. Andrew Gelman and Cosma Tohilla Shalizi, “Philosophy and the Practice of Bayesian Statistics,” British Journal of Mathematical and Statistical Psychology, pp. 1–31, January 11, 2012. http://www.stat.columbia.edu/~gelman/research/published/philosophy.pdf. 63. Although

Red-Blooded Risk: The Secret History of Wall Street

by Aaron Brown and Eric Kim  · 10 Oct 2011  · 483pp  · 141,836 words

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

by Pedro Domingos  · 21 Sep 2015  · 396pp  · 117,149 words

Mastering Pandas

by Femi Anthony  · 21 Jun 2015  · 589pp  · 69,193 words

The Doomsday Calculation: How an Equation That Predicts the Future Is Transforming Everything We Know About Life and the Universe

by William Poundstone  · 3 Jun 2019  · 283pp  · 81,376 words

Architects of Intelligence

by Martin Ford  · 16 Nov 2018  · 586pp  · 186,548 words

Analysis of Financial Time Series

by Ruey S. Tsay  · 14 Oct 2001

The Road to Ruin: The Global Elites' Secret Plan for the Next Financial Crisis

by James Rickards  · 15 Nov 2016  · 354pp  · 105,322 words

On the Edge: The Art of Risking Everything

by Nate Silver  · 12 Aug 2024  · 848pp  · 227,015 words

AIQ: How People and Machines Are Smarter Together

by Nick Polson and James Scott  · 14 May 2018  · 301pp  · 85,126 words

Science Fictions: How Fraud, Bias, Negligence, and Hype Undermine the Search for Truth

by Stuart Ritchie  · 20 Jul 2020

Bulletproof Problem Solving

by Charles Conn and Robert McLean  · 6 Mar 2019

Algorithms to Live By: The Computer Science of Human Decisions

by Brian Christian and Tom Griffiths  · 4 Apr 2016  · 523pp  · 143,139 words

The Organized Mind: Thinking Straight in the Age of Information Overload

by Daniel J. Levitin  · 18 Aug 2014  · 685pp  · 203,949 words

From Bacteria to Bach and Back: The Evolution of Minds

by Daniel C. Dennett  · 7 Feb 2017  · 573pp  · 157,767 words

Why Machines Learn: The Elegant Math Behind Modern AI

by Anil Ananthaswamy  · 15 Jul 2024  · 416pp  · 118,522 words

Thinking, Fast and Slow

by Daniel Kahneman  · 24 Oct 2011  · 654pp  · 191,864 words

Super Thinking: The Big Book of Mental Models

by Gabriel Weinberg and Lauren McCann  · 17 Jun 2019

Superintelligence: Paths, Dangers, Strategies

by Nick Bostrom  · 3 Jun 2014  · 574pp  · 164,509 words

Life Is Simple: How Occam's Razor Set Science Free and Shapes the Universe

by Johnjoe McFadden  · 27 Sep 2021

Doing Data Science: Straight Talk From the Frontline

by Cathy O'Neil and Rachel Schutt  · 8 Oct 2013  · 523pp  · 112,185 words

The Rationalist's Guide to the Galaxy: Superintelligent AI and the Geeks Who Are Trying to Save Humanity's Future

by Tom Chivers  · 12 Jun 2019  · 289pp  · 92,714 words

Against the Gods: The Remarkable Story of Risk

by Peter L. Bernstein  · 23 Aug 1996  · 415pp  · 125,089 words

The Ethical Algorithm: The Science of Socially Aware Algorithm Design

by Michael Kearns and Aaron Roth  · 3 Oct 2019

Hands-On Machine Learning With Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

by Aurelien Geron  · 14 Aug 2019

The Missing Billionaires: A Guide to Better Financial Decisions

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Escape From Model Land: How Mathematical Models Can Lead Us Astray and What We Can Do About It

by Erica Thompson  · 6 Dec 2022  · 250pp  · 79,360 words

Beyond Weird

by Philip Ball  · 22 Mar 2018  · 277pp  · 87,082 words

The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution

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Our Final Invention: Artificial Intelligence and the End of the Human Era

by James Barrat  · 30 Sep 2013  · 294pp  · 81,292 words

Fluke: Chance, Chaos, and Why Everything We Do Matters

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No Slack: The Financial Lives of Low-Income Americans

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Prediction Machines: The Simple Economics of Artificial Intelligence

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The Golden Passport: Harvard Business School, the Limits of Capitalism, and the Moral Failure of the MBA Elite

by Duff McDonald  · 24 Apr 2017  · 827pp  · 239,762 words

The Moral Landscape: How Science Can Determine Human Values

by Sam Harris  · 5 Oct 2010  · 412pp  · 115,266 words

Little Brother

by Cory Doctorow  · 29 Apr 2008  · 398pp  · 120,801 words

Succeeding With AI: How to Make AI Work for Your Business

by Veljko Krunic  · 29 Mar 2020

The Hidden Half: How the World Conceals Its Secrets

by Michael Blastland  · 3 Apr 2019  · 290pp  · 82,871 words

The End of College: Creating the Future of Learning and the University of Everywhere

by Kevin Carey  · 3 Mar 2015  · 319pp  · 90,965 words

The Idealist: Aaron Swartz and the Rise of Free Culture on the Internet

by Justin Peters  · 11 Feb 2013  · 397pp  · 102,910 words

Dinosaurs Rediscovered

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Learn Algorithmic Trading

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Statistics hacks

by Bruce Frey  · 9 May 2006  · 755pp  · 121,290 words

Statistics in a Nutshell

by Sarah Boslaugh  · 10 Nov 2012

The Genetic Lottery: Why DNA Matters for Social Equality

by Kathryn Paige Harden  · 20 Sep 2021  · 375pp  · 102,166 words

The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences

by Rob Kitchin  · 25 Aug 2014

The Rise of the Quants: Marschak, Sharpe, Black, Scholes and Merton

by Colin Read  · 16 Jul 2012  · 206pp  · 70,924 words

Natural Language Processing with Python and spaCy

by Yuli Vasiliev  · 2 Apr 2020

The Loop: How Technology Is Creating a World Without Choices and How to Fight Back

by Jacob Ward  · 25 Jan 2022  · 292pp  · 94,660 words

The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity

by Amy Webb  · 5 Mar 2019  · 340pp  · 97,723 words

Reinventing Capitalism in the Age of Big Data

by Viktor Mayer-Schönberger and Thomas Ramge  · 27 Feb 2018  · 267pp  · 72,552 words

The Crux

by Richard Rumelt  · 27 Apr 2022  · 363pp  · 109,834 words

The Antisocial Network: The GameStop Short Squeeze and the Ragtag Group of Amateur Traders That Brought Wall Street to Its Knees

by Ben Mezrich  · 6 Sep 2021  · 239pp  · 74,845 words

Futureproof: 9 Rules for Humans in the Age of Automation

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Tribe of Mentors: Short Life Advice From the Best in the World

by Timothy Ferriss  · 14 Jun 2017  · 579pp  · 183,063 words