Praise for the first edition: Principles of Uncertainty is a profound and mesmerising book on the foundations and principles of subjectivist or behaviouristic Bayesian analysis. ... the book is a pleasure to read. And highly recommended for teaching as it can be used at many different levels. ... A must-read for sure!-Christian Robert, CHANCEIt's a lovely book, one that I hope will be widely adopted as a course textbook. -Michael Jordan, University of California, Berkeley, USA Like the prize-winning first edition, Principles of Uncertainty, Second Edition is an accessible, comprehensive text on the theory of Bayesian Statistics written in an appealing, inviting style, and packed with interesting examples. It presents an introduction to the subjective Bayesian approach which has played a pivotal role in game theory, economics, and the recent boom in Markov Chain Monte Carlo methods. This new edition has been updated throughout and features new material on Nonparametric Bayesian Methods, the Dirichlet distribution, a simple proof of the central limit theorem, and new problems. Key Features: First edition won the 2011 DeGroot Prize Well-written introduction to theory of Bayesian statistics Each of the introductory chapters begins by introducing one new concept or assumption Uses "just-in-time mathematics"-the introduction to mathematical ideas just before they are applied

A fair question to ask of an advocate of subjective Bayesianism (which the author is) is "how would you model uncertainty?" In this book, the author writes about how he has done it using real problems from the past, and offers additional comments about the context in which he was working.

The book will serve primarily as a user's manual or desk reference for the expert witness-lawyer team and secondarily as a textbook or supplemental textbook for upper level undergraduate statistics students. It starts with two articles by masters of the trade, Paul Meier and Franklin Fisher. It then explains the distinction between the Frye and Daughbert standards for expert testimony, and how these standards play out in court. The bulk of the book is concerned with individual cases ranging over a wide variety of topics, such as electronic draw poker (does it require skill to play), employment discrimination (how to tell whether an employer discriminated against older workers in deciding whom to fire), driving while black (did the New Jersey State Police disproportionately stop blacks), jury representativeness (is a jury a representative cross section of the community), juries hearing death penalty cases (are such juries biased toward a guilty verdict, and does the Supreme Court care), the civil incarceration of violent sexual offenders after having served their jail sentences (can future dangerousness be predicted), do data from multiple choice examinations support an allegation of copying, whether rental agents in an apartment complex steered African-American prospects to one part of the complex, how much tax is owed after an audit that used a random sample, whether an inventor falsified his notebook in an effort to fool the Patent Office, and whether ballots had been tampered with in an election. The book concludes with two recent English cases, one in which a woman was accused of murdering her infant sons because both died of "cot death" or "sudden death syndrome", (she was convicted, but later exonerated), and how Bayesian analyses can (or more precisely), cannot be presented in UK courts. In each study, the statistical analysis is shaped to address the relevant legal questions, and draws on whatever methods in statistics might shed light on those questions.

This important collection of essays is a synthesis of foundational studies in Bayesian decision theory and statistics. An overarching topic of the collection is understanding how the norms for Bayesian decision making should apply in settings with more than one rational decision maker and then tracing out some of the consequences of this turn for Bayesian statistics. The volume will be particularly valuable to philosophers concerned with decision theory, probability, and statistics, statisticians, mathematicians, and economists.

A fair question to ask of an advocate of subjective Bayesianism (which the author is) is "how would you model uncertainty?" In this book, the author writes about how he has done it using real problems from the past, and offers additional comments about the context in which he was working.

This important collection of essays is a synthesis of foundational studies in Bayesian decision theory and statistics. An overarching topic of the collection is understanding how the norms for Bayesian decision making should apply in settings with more than one rational decision maker and then tracing out some of the consequences of this turn for Bayesian statistics. There are four principal themes to the collection: cooperative, non-sequential decisions; the representation and measurement of 'partially ordered' preferences; non-cooperative, sequential decisions; and pooling rules and Bayesian dynamics for sets of probabilities. The volume will be particularly valuable to philosophers concerned with decision theory, probability, and statistics, statisticians, mathematicians, and economists.