Everything Is Predictable How Bayesian Statistics Explain Our World Pdf


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Everything Is Predictable


Everything Is Predictable

Author: Tom Chivers

language: en

Publisher: Simon and Schuster

Release Date: 2024-05-07


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A “fascinating, witty, and perspective-shifting” (Oliver Burkeman, New York Times bestselling author) tour of Bayes’s theorem and its global impact on modern life from the acclaimed science writer and author of The Rationalist’s Guide to the Galaxy. At its simplest, Bayes’s theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event. But in Everything Is Predictable, Tom Chivers lays out how it affects every aspect of our lives. He explains why highly accurate screening tests can lead to false positives and how a failure to account for it in court has put innocent people in jail. A cornerstone of rational thought, many argue that Bayes’s theorem is a description of almost everything. But who was the man who lent his name to this theorem? How did an 18th-century Presbyterian minister and amateur mathematician uncover a theorem that would affect fields as diverse as medicine, law, and artificial intelligence? “Witty, lively, and best of all, extremely nerdy” (Tim Harford, author of The Undercover Economist), Everything Is Predictable is an entertaining and accessible illustration of how a single compelling idea can have far reaching consequences.

Everything Is Predictable


Everything Is Predictable

Author: Tom Chivers

language: en

Publisher: Simon and Schuster

Release Date: 2025-07-08


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A “fascinating, witty, and perspective-shifting” (Oliver Burkeman, New York Times bestselling author) tour of Bayes’s theorem and its global impact on modern life from the acclaimed science writer and author of The Rationalist’s Guide to the Galaxy. At its simplest, Bayes’s theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event. But in Everything Is Predictable, Tom Chivers lays out how it affects every aspect of our lives. He explains why highly accurate screening tests can lead to false positives and how a failure to account for it in court has put innocent people in jail. A cornerstone of rational thought, many argue that Bayes’s theorem is a description of almost everything. But who was the man who lent his name to this theorem? How did an 18th-century Presbyterian minister and amateur mathematician uncover a theorem that would affect fields as diverse as medicine, law, and artificial intelligence? “Witty, lively, and best of all, extremely nerdy” (Tim Harford, author of The Undercover Economist), Everything Is Predictable is an entertaining and accessible illustration of how a single compelling idea can have far reaching consequences.

Bayesian Data Analysis, Third Edition


Bayesian Data Analysis, Third Edition

Author: Andrew Gelman

language: en

Publisher: CRC Press

Release Date: 2013-11-01


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Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.