A First Course in Bayesian Statistical Methods (Springer Texts in Statistics) by Peter D. Hoff (PDF)

Description

A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run “as-is” allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.

User’s Reviews

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

⭐I am a first year student in a PhD program in statistics with near zero statistics background prior to this year. I must say, this is probably the best balance of theory/applications I’ve come across thus far. The proofs are rigorous but not untouchable to many non-statistics students in the course (ie, epidemiology and related fields requiring a statistics background). Book is thorough and the applications/figures well-motivate the topic area. Definitely a great book to read through and then keep on the shelf.Content wise, when I first got the book, naturally I paged to the end and legitimately the entire thing looked foreign/illegible to me more or less it seemed so advanced. However, PHoff presents the material in such a way that he provides just enough theory to make the results feel intuitive but not cumbersome, which really helps downstream with your ability to recall and actually APPLY the results within the book. The book basically takes you from zero bayesian background to understanding, coding, and applying fairly rigorous techniques for seemingly cumbersome statistical models that will feel intuitive to you by the end.Only drawback to the book is that there is no second edition. There are a number of errors throughout the book that definitely detract somewhat from the content. However, if you check PHoff’s website, he has noted (as far as I can tell) all of them in annotations by page, which helps this issue a bit. Also, a “first course in bayesian statistics” as per the preface assumes you have a very solid understanding of fundamentals of probability and statistics (NOT that you know how to use a normal distribution, or other statistical results, you should be familiar with theoretical fundamentals for this book to make sense). Hence it is a first course in bayesian statistics, and not a first course in statistical theory. Particularly, you should definitely be very comfortable with Bayes’ rule, and how to manipulate around probability statements using Bayes’ rule and definitions of conditional probability fairly seamlessly, before giving the book a try as it assumes you are comfortable with those concepts going in. Also, experience with frequentist inference would be useful for putting Bayesian inference into perspective.

⭐I have read the first 11 chapters of this book so far and found it pretty good. Studying this book doesn’t require advanced mathematical knowledge such as measure theory which makes it suitable for a wide range of people who want to know more about the Bayesian framework. However, familiarity with basic Matrix Algebra and probability is somewhat required. In my opinion, the biggest advantage of this book is that it provides the reader with a deep understanding of Bayesian procedure concepts rather than purely mathematical formulation. In addition, the R code for all the figures is provided on the author’s webpage which is another advantage. Other than some minor typos, I cannot think of any noticeable drawbacks which you can also find a list of them on the book’s webpage as well.

⭐This is an appropriate practical introduction to Bayesian methods for someone who has taken both a college-level probability and statistics course. The multidimensional examples may require a bit of linear algebra. It doesn’t include much comparison with frequentist techniques, so some familiarity there would help the reader put the ideas in context.Compared to a book like Christian Robert’s excellent _The Bayesian Choice_, this book may appear inadequate, because it is less than half the size, is often less dense and scholarly, and is (currently at Amazon) almost double the price. However, I’m happy I have both because Hoff’s book is more practical for someone who actually wants to use Bayesian statistics in practical situations. Hoff spends a lot of time discussing simple examples with wide application, and he actually shows the R code to compute the answers with MCMC techniques.However, after reading the book, I still don’t feel totally prepared to apply R in real-life Bayesian situations. It would be nice for a practical book like Hoff’s to include some hands-on tips about how to do these problems (R packages to use, basic modeling strategies, common pitfalls, speed concerns, assessing convergence, etc.).

⭐It is a very decent book for Bayesian theory by Peter Hoff, professor of statistics at UofW. It is definitely not the most advanced book you can find in Bayesian theory but it covers some good topics and gives some good intuition to people who just started to think Bayesian. A nice thing about this book is that it has R codes available so it is a good source to learn R from. This book has some typos but it is still worth having the book and reading through it. This book is best for senior undergraduates in statistics or master students in engineering.

⭐This is a great introductory book for someone wanting an understandable, practical overview of the subject.I am currently half-way through the book, but have so far been very pleased. The book has clear and useful examples scattered throughout the chapters which help illuminate the ideas and procedures (I am doing this self-paced, not as part of a course). The book also appears to cover a wide variety of topics; several of my coworkers are very familiar with Bayesian Statistics and looking through the table of contents they seemed interested in reading it after I was done.Overall, I find this a good intro book. It doesn’t focus on proofs and theorems, but on using the mechanisms presented.

⭐Decent book but there are errors. Check the errata online.

⭐Good Bayesian stat book but not really intro…Concepts are clear and examples are decent.But this is not an easy book. Definitely not intro.

⭐Nothing to say about the article itself. I wish it was a little bit conceptually clearer, more explanations and reiterate some ideas would have been good for learning. Aesthetic come first of all, sometimes before than functionality as well (this is not necessary in my opinion). It would be useful add some R code and talk about Bayesian statistics not supposing readers are experienced about the subject.

⭐Too hard for an introduction to the subject. More suitable as the text for a graduate course.

⭐ベイズ統計学の入門書。共役事前分布を仮定した際の事後分布の公式と簡単なモンテカルロシミュレーションの解説が第5章まで。第6章でギプスサンプラーが解説されます。第7-9章では多変量正規分布、階層正規モデル、線形回帰が説明され、第10章はMHアルゴリズムが、11章以降では一般化線形モデルなどが解説されます。ページ数の割りには内容が豊富で、説明も丁寧です。数式の解説が適度に詳しくわかりやすいのがよかったです。（他の本などでは「演習問題」にまわされてしまうような証明も初心者にわかりやすく書かれています）実装にはRが使用され、詳しいコードが書いてあり、応用する際の参考になります。Rコードの説明も丁寧なのでR初心者ですがストレスなく読み進めました。本文中のデータと図などのRコードは著者のウェブサイトからDLできます。MCMCではWinBUGSは使用せずにすべてRで手書きするようになっていますが、かえって理解にはよかったと思います。一般化線形モデルの解説少し駆け足なのと、問題の解答がないのが残念ですが、本書は入門書としても実用の手引きとしてもお勧めだと思います。

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