PDF Download Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians (Chapman & Hall/CRC Texts in Statistical Science)
PDF Download Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians (Chapman & Hall/CRC Texts in Statistical Science)
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Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians (Chapman & Hall/CRC Texts in Statistical Science)
PDF Download Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians (Chapman & Hall/CRC Texts in Statistical Science)
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Review
This book provides a good introduction to Bayesian approaches to applied statistical modelling. … The authors have fulfilled their main aim of introducing Bayesian ideas through examples using a large number of statistical models. An interesting feature of this book is the humour of the authors that make it more fun than typical statistics books. In summary, this is a very interesting introductory book, very well organised and has been written in a style that is extremely pleasant and enjoyable to read. Both the statistical concepts and examples are very well explained. In conclusion, I highly recommend this book as both a M.S./Ph.D. course text and as an excellent reference book for anyone interested in Bayesian statistics. A copy of it should certainly appear in every university or, even private, library.―Rolando de la Cruz, Journal of Applied Statistics, June 2012 Bayesian Ideas and Data Analysis (BIDA) is exactly what its title advertises: an introduction to Bayesian approaches to applied statistical modeling. Its authors, who are renowned Bayesian statisticians, present a variety of insightful case studies of Bayesian data analysis, many of which have been drawn from their own research. The book is an excellent purchase for practitioners who are unfamiliar with Bayesian methods and want to learn to use them for their data-based research. BIDA also should be strongly considered as a primary text by teachers of introductory courses in applied Bayesian inference. … The writing in BIDA is clear, accurate, and easy to follow.―Jerome P. Reiter, The American Statistician, November 2011 I liked it very much! … the book is indeed focused on explaining the Bayesian ideas through (real) examples and it covers a lot of regression models, all the way to non-parametrics. It contains a good proportion of WinBUGS and R codes. … The book is pleasant to read, with humorous comments here and there. …―Christian Robert (Université Paris-Dauphine) on his blog, October 2011 If you think that a Bayesian approach to statistical analysis is nice in principle but too complicated in practice, this book may change your mind. The authors’ enthusiasm for the subject is apparent and they have taken care that the text is generally easy to read, with some occasional wry comments that make it more amusing than a typical statistics book. The emphasis is on medical and biological cases, but a range of other applications are covered. … There are three useful appendices on matrices and vectors, probability, and getting started in R, which is well chosen, and includes a note on the interface between R and WinBUGS. The exercises are an integral part of the book and are placed throughout the text … I think that the book is innovative for two reasons. Firstly, it provides an intermediate-level course in statistics, using the Bayesian paradigm, that could be given to engineers and scientists requiring substantial statistical analysis, as well as material for a course in Bayesian statistics that is typically offered to statistics students. Secondly, it shows how to perform the analyses by using WinBUGS throughout the text. I would use this book as a basis for a course on Bayesian statistics. It is an excellent text for individual study, and students will find it a valuable reference later in their careers.―Andrew V. Metcalfe, Journal of the Royal Statistical Society: Series A, Vol. 174, October 2011 I do believe this book to be more accessible that most Bayesian books … this book could be adequate for the statistics student who has a solid background in statistical concepts and wants to gain more knowledge about the Bayesian approach. … The authors do a good job of providing examples … There are a number of exercises included, which makes the book adequate as a textbook. … There are many samples of WinBUGS code interspersed throughout for the different data examples, which are valuable for someone trying to implement Bayesian methods for data analysis. I found the book easy to read and there are more attempts to liven up the book with humor than the typical textbook.―Willis A. Jensen, Journal of Quality Technology, Vol. 43, No. 2, April 2011 This is a very sound introductory text, and is certainly one which teachers of any course on Bayesian statistics beyond the briefest and most elementary should consider adopting.―David J. Hand, International Statistical Review (2011), 79 Unlike many Bayesian books which did not cover this topic extensively, this new book teaches readers how to illicit informative priors from field experts in great detail. … Straightforward R codes are also provided for pinpointing hyperparameter values … this book is particularly valuable in emphasizing the right approach to elicit prior, an important component of deriving posterior or predictive distribution. Another important feature of this new Bayesian textbook is its rich details. …The proofs never skip steps, and are easy to follow for readers taking only one or two semester math stat classes. The well-written text along with more than 70 figures and 50 plus tables add tremendously to the elucidation of the problems discussed in the book. Directly following some examples or important discussion in the text, readers can self-check whether they understand the materials by playing with some exercise problems, most of which are pretty straightforward.Christensen et al. provide many WinBUGS codes in the book and a website for readers to download these codes. In addition, the authors introduce how to perform Bayesian inferences using SAS codes on two occasions … The book also recommends some other programs or websites that will facilitate computation … This book is also characterized by its humor, … [making] reading this Bayesian book more delightful.―Dunlei Cheng, Statistics in Medicine, 2011
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About the Author
Ronald Christensen is a Professor in the Department of Mathematics and Statistics at the University of New Mexico, Albuquerque. He is also a Fellow of the American Statistical Association (ASA) and the Institute of Mathematical Statistics as well as the former Chair of the ASA Section on Bayesian Statistical Science. Wesley Johnson is a Professor in the Department of Statistics at the University of California, Irvine. He is also a Fellow of the ASA and Chair-Elect of the ASA Section on Bayesian Statistical Science. Adam Branscum is an Associate Professor in the Department of Public Health at Oregon State University, Corvallis. Timothy E. Hanson is an Associate Professor in the Department of Statistics at the University of South Carolina, Columbia.
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Product details
Series: Chapman & Hall/CRC Texts in Statistical Science
Hardcover: 516 pages
Publisher: CRC Press; 1 edition (July 2, 2010)
Language: English
ISBN-10: 1439803544
ISBN-13: 978-1439803547
Product Dimensions:
6.9 x 1.1 x 10 inches
Shipping Weight: 2.4 pounds (View shipping rates and policies)
Average Customer Review:
3.6 out of 5 stars
6 customer reviews
Amazon Best Sellers Rank:
#423,378 in Books (See Top 100 in Books)
Hands down this is the best applied Bayesian book out there. I read the most of this book (14 out of 15 chapters). For the first time I have seen a book pinpointing the details of inducing prior information. It is very PRACTICAL. It deepens your understanding of Bayesian analysis. It has been fun trying to adopt their prior elicitation methods to my data-sets and see the actual changes from prior to posterior distribution. Now I can collaborate with non-statisticians and convince them to use use Bayesian ideas. By the way most of the books (such as Gelmans) assume non-infromative prior most of the time or at best they suggest "ask an expert" and that is it.Other highlighting points 1)Using their winbugs code and the ideas in the book, I have repeated many of my frequentest analysis using now Bayesian analysis. 2) It has sufficient amount of theoretical material for such introductory book especially in chapters 4 and 6 (you can skip them if you want). 3) Mixed models. I always hated performing GLMM using frequnetest approach (it is not convincing!) . It is straightforward to fit GLMM using Bayesian approach. This is outlined in the book. 4) Easy to follow non-parametric chapter . 5) Practical case studies and examples.You can read my review about the Gelamn's book at its place, for now it is suffice to say this book is superior. I understand if someone gets frustrated after the first couple readings. Just give yourself some time and you will start to like its style after that.
If you don't mind the notation the authors use, this book will be a great introduction. I have Bayesian Data Analysis by Gelman et. al., and in comparison, Christiensen et. al. provide a much more concrete introduction to the mathematics behind the derivations of the posteriors used. Additionally, there is some useful R and WinBUGS code in the book. I didn't use it, but it may be useful for others.Note: I have a MS in Statistics. I suggest this book if you have a good understanding of algebra with matrices (not just linear algebra), and integration.
The text is deep, and includes many aspects of Bayesian analysis which are not covered in classical presentations. That said, the presentations are tentative, and do not reflect the warm reception these techniques have received in many scientific and technical fields. That reception exists because the methods are powerful, solving problems hitherto unapproachable, and because techniques for calculating Bayesian approaches are becoming ever more available.This text presents some of these, but ends up being more of a sketch than a fundamental contribution.
I think Bayesian data analysis is better than this
I found several typos in this text. Mid-way through my semester in Applied Bayesian Analysis I put this book aside and picked up Bayesian Data Analysis by Gelman, which was much better.
This is my textbook for bayesian analysis.It's a new one.I buy this new one since the price of used books is higher than this one
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