Bayesian Statistics: An Introduction 4th Edition by Peter M. Lee (PDF)

Description

Bayesian Statistics is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs. The first edition of Peter Lee’s book appeared in 1989, but the subject has moved ever onwards, with increasing emphasis on Monte Carlo based techniques.This new fourth edition looks at recent techniques such as variational methods, Bayesian importance sampling, approximate Bayesian computation and Reversible Jump Markov Chain Monte Carlo (RJMCMC), providing a concise account of the way in which the Bayesian approach to statistics develops as well as how it contrasts with the conventional approach. The theory is built up step by step, and important notions such as sufficiency are brought out of a discussion of the salient features of specific examples.This edition:Includes expanded coverage of Gibbs sampling, including more numerical examples and treatments of OpenBUGS, R2WinBUGS and R2OpenBUGS.Presents significant new material on recent techniques such as Bayesian importance sampling, variational Bayes, Approximate Bayesian Computation (ABC) and Reversible Jump Markov Chain Monte Carlo (RJMCMC).Provides extensive examples throughout the book to complement the theory presented.Accompanied by a supporting website featuring new material and solutions.More and more students are realizing that they need to learn Bayesian statistics to meet their academic and professional goals. This book is best suited for use as a main text in courses on Bayesian statistics for third and fourth year undergraduates and postgraduate students.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐This text comes with a companion website that provides solutions to the chapter exercises (about 18 per chapter) and also addresses the advanced Mathematics involved in proof of Bayesian theorems. I for one always appreciate having something against which to check my research and work. The book is a solid rock of reference and information.

⭐I gave the highest rating to the Lee book because he develops the subject matter systematically, and there are no gaps in his reasoning. Moreover, the author’s language is precise, and he supplies the motivation for each new concept. The companion website for this book will ensure your success, because solutions to various exercises are presented. All in all, the resources provided by Lee in this compact textbook are more than adequate to make a serious student do well in homework and exams. However, this is a textbook suitable only for those who have sufficient background in Probability and Calculus.

⭐I thought it is a good book, but I was wrong. There are lots of typo, and the statistical symbols in this book are rarely found in other books. It makes confusions when you try to connect between the knowledge you’ve learn and the material in this book.

⭐While the book itself is fantastic, the Kindle version is almost unreadable. The equations are almost unreadable and navigation is also almost impossible. One of the worst Kindle books I have seen.

⭐I find it hard to follow. Every time I pick it to read about a certain concept, I find the explanation unclear and hard to grasp.

⭐I have studied Bayesian statistics at master’s degree level and now teach it to undergraduates. I have always recommended Lee’s book as background reading for my students because of its very clear, concise and well organised exposition of Bayesian statistics.It has improved significantly with every edition and now offers a remarkably complete coverage of Bayesian statistics for such a relatively small book. For example, it has a short but excellent section on decision theory, it covers Bayesian regression and multi-level models well and it has extended coverage of MCMC methods (Gibbs sampling, Metropolis Hastings).I disagree with some reviewers of previous editions who say that it contains ‘typos’. It contains no more errors than any other maths book but the notation it uses (whilst being standard) may not be familiar to readers who do not have sufficient background in mathematics.I would now definitely recommend it as my first choice of books for undergraduates who are studying Bayesian statistics for the first time (and it is a good introduction for anybody with a good background in mathematical statistics (including probability distribution theory and likelihood based inference) plus first year university calculus).[PS For people who want to understand Bayesian statistics but who do not have the necessary background in mathematical statistics and calculus I would very strongly recommend “The theory that would not die” by Sharon Bertsch Mcgrayne (Yale), which is a non-mathematical survey of the history and principles of Bayesian statistics or “Doing Bayesian Data Analysis” by John Kruschke (Academic Press) which is a very detailed and thorough introduction to Bayesian methods using only basic mathematics. The standard textbook in Bayesian Statistics is “Bayesian Data Analysis” (often known simply as “BDA”) by Gelman, Carlin et. al (Chapman and Hall/CRC)” but Lee’s book takes a more mathematical approach, is more concise and, in spite of being smaller, is just as comprehensive in its coverage.]

⭐who really want to learn and apply Bayesian statistics. The text is not for those who want a “quick” or a “brief” or a “for the layperson” intorduction.

⭐Um excelente texto em que as ideias básicas da estatistica bayesiana são apresentadas, com um bom número de exemplos ilustrativos. Acho in excelente livro que pode ser utilizado para quem já domina alguns conceitos básicos de estatística e deseja aprofundá-los com ênfase na estatística bayesiana.

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