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A First Course in Bayesian Statistical Methods (Springer Texts in Statistics), by Peter D. Hoff
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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.
- Sales Rank: #1056537 in Books
- Published on: 2010-11-09
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .64" w x 6.10" l, .88 pounds
- Binding: Paperback
- 271 pages
Review
From the reviews:
This is an excellent book for its intended audience: statisticians who wish to learn Bayesian methods. Although designed for a statistics audience, it would also be a good book for econometricians who have been trained in frequentist methods, but wish to learn Bayes. In relatively few pages, it takes the reader through a vast amount of material, beginning with deep issues in statistical methodology such as de Finetti’s theorem, through the nitty-gritty of Bayesian computation to sophisticated models such as generalized linear mixed effects models and copulas. And it does so in a simple manner, always drawing parallels and contrasts between Bayesian and frequentist methods, so as to allow the reader to see the similarities and differences with clarity. (Econometrics Journal) “Generally, I think this is an excellent choice for a text for a one-semester Bayesian Course. It provides a good overview of the basic tenets of Bayesian thinking for the common one and two parameter distributions and gives introductions to Bayesian regression, multivariate-response modeling, hierarchical modeling, and mixed effects models. The book includes an ample collection of exercises for all the chapters. A strength of the book is its good discussion of Gibbs sampling and Metropolis-Hastings algorithms. The author goes beyond a description of the MCMC algorithms, but also provides insight into why the algorithms work. …I believe this text would be an excellent choice for my Bayesian class since it seems to cover a good number of introductory topics and giv the student a good introduction to the modern computational tools for Bayesian inference with illustrations using R.�(Journal of the American Statistical Association, June 2010, Vol. 105, No. 490)
“Statisticians and applied scientists. The book is accessible to readers having a basic familiarity with probability theory and grounding statistical methods. The author has succeeded in writing an acceptable introduction to the theory and application of Bayesian statistical methods which is modern and covers both the theory and practice. … this book can be useful as a quick introduction to Bayesian methods for self study. In addition, I highly recommend this book as a text for a course for Bayesian statistics.” (Lasse Koskinen, International Statistical Review, Vol. 78 (1), 2010)
“The book under review covers a balanced choice of topics … presented with a focus on the interplay between Bayesian thinking and the underlying mathematical concepts. … the book by Peter D. Hoff appears to be an excellent choice for a main reading in an introductory course. After studying this text the student can go in a direction of his liking at the graduate level.” (Krzysztof Łatuszyński, Mathematical Reviews, Issue 2011 m)
“The book is a good introductory treatment of methods of Bayes analysis. It should especially appeal to the reader who has had some statistical courses in estimation and modeling, and wants to understand the Bayesian interpretation of those methods. Also, readers who are primarily interested in modeling data and who are working in areas outside of statistics should find this to be a good reference book. … should appeal to the reader who wants to keep with modern approaches to data analysis.” (Richard P. Heydorn, Technometrics, Vol. 54 (1), February, 2012)
From the Back Cover
This book provides a compact self-contained introduction to the theory and application of Bayesian statistical methods. The book is accessible to readers having a basic familiarity with probability, yet allows more advanced readers to quickly grasp the principles underlying Bayesian theory and methods. The examples and computer code allow the reader to understand and implement basic Bayesian data analyses using standard statistical models and to extend the standard models to specialized data analysis situations. The book begins with fundamental notions such as probability, exchangeability and Bayes' rule, and ends with modern topics such as variable selection in regression, generalized linear mixed effects models, and semiparametric copula estimation. Numerous examples from the social, biological and physical sciences show how to implement these methodologies in practice.
Monte Carlo summaries of posterior distributions play an important role in Bayesian data analysis. The open-source R statistical computing environment provides sufficient functionality to make Monte Carlo estimation very easy for a large number of statistical models and example R-code is provided throughout the text. Much of the example code can be run ``as is'' in R, and essentially all of it can be run after downloading the relevant datasets from the companion website for this book.
Peter Hoff is an Associate Professor of Statistics and Biostatistics at the University of Washington. He has developed a variety of Bayesian methods for multivariate data, including covariance and copula estimation, cluster analysis, mixture modeling and social network analysis. He is on the editorial board of the Annals of Applied Statistics.
Most helpful customer reviews
26 of 27 people found the following review helpful.
Good hands-on introduction
By Grue
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.).
11 of 11 people found the following review helpful.
Do not get the kindle version
By Ryan Brady
The text is fine, although as another reviewer mentioned - the homework is frustrating. None of it is the sort of problem where you can go back and follow along with work done in the chapter. Not at all good for someone who prefers to learn by first mimicking, and then exploring.
No, the BIG problem is that many of the formulas are stored as images since equations are incompatible with the file format. If you try to increase the text size so you can actually read them, the formulas stay the same tiny size. Trying to use the windows accessibility tools just results in a pixelated illegible formula.
I would love to have all my texts in electronic format, but not until this issue can be fixed.
8 of 8 people found the following review helpful.
Solid Introduction
By Jared Becksfort
I used this book to learn about Bayesian statistics on my own, and I was able to successfully perform Bayesian analyses at work afterward. I really had very little idea of what Bayesian statistics was (or is? I am not an English major) before reading it, but I feel I have a good understanding of its basics now. The book is strongly mathematical, and it has helped me understand statistical papers from JASA. It should definitely not be your first statistics book.
Some criticisms: there are some good examples, but not as many as I would have liked. The exercises in the back of the book do not have solutions available, to my knowledge. Some well-known topics such as Jeffrey's priors are only discussed in the exercises.
The title of the book sells itself short a bit. R is used heavily from about the 3rd chapter onward. There is a ton of R code available at the author's website, much of which is not shown in the book. A pleasant surprise to me was that all the code used to generate every figure from the book in PDF form is available at the web site too. I learned a lot about graphical programming in R by going through it.
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