PDF Download Doing Bayesian Data Analysis: A Tutorial with R and BUGS

PDF Download Doing Bayesian Data Analysis: A Tutorial with R and BUGS

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Doing Bayesian Data Analysis: A Tutorial with R and BUGS

PDF Download Doing Bayesian Data Analysis: A Tutorial with R and BUGS

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Review

"This book is head-and-shoulders better than the others I've seen. I'm using it myself right now. Here's what's good about it: •It builds from very simple foundations. •Math is minimized. No proofs. •From start to finish, everything is demonstrated through R programs. •It helps you learn Empirical Bayesian methods from every angle…"--Exploring Possibility Space blog, March 12, 2014

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From the Back Cover

There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, provides an accessible approach to Bayesian Data Analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data. The text delivers comprehensive coverage of all scenarios addressed by non-Bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, correlation, multiple regression, and chi-square (contingency table analysis). Â This book is intended for first year graduate students or advanced undergraduates. It provides a bridge between undergraduate training and modern Bayesian methods for data analysis, which is becoming the accepted research standard. Prerequisite is knowledge of algebra and basic calculus. |There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, provides an accessible approach to Bayesian Data Analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data. The text delivers comprehensive coverage of all scenarios addressed by non-Bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, correlation, multiple regression, and chi-square (contingency table analysis). This book is intended for first year graduate students or advanced undergraduates. It provides a bridge between undergraduate training and modern Bayesian methods for data analysis, which is becoming the accepted research standard. Prerequisite is knowledge of algebra and basic calculus.

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Product details

Hardcover: 672 pages

Publisher: Academic Press; 1 edition (November 10, 2010)

Language: English

ISBN-10: 0123814855

ISBN-13: 978-0123814852

Product Dimensions:

7.6 x 1.3 x 9.3 inches

Shipping Weight: 2.8 pounds

Average Customer Review:

4.7 out of 5 stars

60 customer reviews

Amazon Best Sellers Rank:

#853,123 in Books (See Top 100 in Books)

I highly recommend this book to two audiences: (a) instructors looking to construct a strong course on "introduction to social science statistics" from a Bayesian perspective; and (b) social science researchers who have been educated in a classical framework and wish to learn the foundational knowledge of a Bayesian approach, without a refresher in differential calculus. (I expect it would also of interest to many physical science and engineering researchers whose methods are not highly divergent from social science (e.g., biologists, operations engineers) but I can't speak authoritatively about that.)I'm a practicing social science researcher and have wanted for years to learn Bayesian methods deeply - I've used them in applied settings but without complete understanding. My quest to learn Bayesian methods more rigorously has been persistently stymied by texts that demand analytic solutions to prior/posterior estimation, that are excruciatingly focused on specific problems with little attention to generalization, or that skip huge areas of exposition to leap from a toy problem to a complex one with little clue of the path between them. Dr. Kruschke's text avoids all of those problems. It is remarkable for building intuition from basic principles, for avoiding page-after-page of integrals, and for having extremely clear application.The book starts by laying out the core intuitions of Bayes's rule - instead of merely stating it (and don't we all think we know it by now?), it leads the reader through some applied examples with frequency tables. Simple? Yes; but also valuable to force oneself through. It then builds upon this knowledge systematically, going through the requisite coin toss examples - but unlike most texts, connecting them clearly to real-world examples of binomial problems. And it proceeds from there, ending up with Bayesian versions of ANOVA-type problems and logistic regression.There are two other salient and important features of the book. First, the exercises are particularly well-chosen to reinforce the key points and demonstrate applications. I strongly recommend to work your way through them. In my case, for instance, they forced me to confront understanding of things like the "prior likelihood of the data" - a core concept that I thought I understood but really didn't until I had to solve some actual problems.Second, the book is closely linked to the R statistics environment - surely the most popular tool used by Bayesian statisticians - and has sample programs that are illustrative, useful, and actually work. If you do Bayesian work, you're probably going to use R, and these examples will help immensely to build the set of tools you'll need.Finally, and just to make clear, I have a disrecommendation for one audience: if you're looking for a highly mathematical treatment of Bayesian methods, it is not the right book. It is a didactic text, not a reference manual or set of derivations.Good luck to you as a reader, and thank you to the author!

I love this book for a number of reasons, but one of which having to do with the time the author takes to explain all sorts of critical nuances I don't see addressed elsewhere that I have bugged me for years (not being a professional statistician). I dearly appreciate this kind of empathy on the part of an author!

If you want to learn Bayesian stats on your own, then this is a good book to learn it from. But, take your time. There's a wealth of information in the text, and if you move too fast (personal experience), then you will pay a penalty.One thing I have never understood is WinBUGS. It's used in the book and I can't believe that this is the "best" software available. This is not a fault of the book though.I liked the explanations overall, but the only shortcoming is sometimes the results of some of the calculations were limited.

This is a great book.If you use R and have interest in Bayes, you should buy this book.The style is more pragmatic, less academic than Wiley texts.The R code provided is clear, well written and demonstrative.Further, the author provides a streamlined agenda, like, if you want a quick overview, here are the chapters to read: ...This was considerate.I found this book so interesting I wound up reading the interim materials as well.

I use this book as a recommended text in both classes (an "advanced data analysis and applied regression" PhD seminar) and with my research assistants. This is definitely the most accessible text on Bayesian methods for psychologists. The numerous worked examples, code in R, BUGS, and JAGS, and the solution manual available from the author's website, all make this book excellent for self-guided study. You would have to be actively resisting it to not learn Bayesian data analysis! The writing is always completely clear, and the methods are built up from the simplest to very complex in intuitive steps. Assumes essentially no background beyond fairly basic algebra and just enough calculus to more-or-less remember what an integral does. I believe it could be used as a first statistics book, if the instructor didn't feel the need to inculcate traditional frequency-based methods first!

This book is extremely well written for the autodidact. His writing style is extremely clear, witty, and amusing. Some downsides are BUGS is a pain in the arse to work with, so a lot of the programming exercises become very difficult, and he doesn't really spend a whole lot of time introducing you to the workings of the R programming language properly. In retrospect, I would purchase a book like Learning R by Richard Cotton first, work through it, then tackle this book. But as far as the BUGS problems goes, the author has recommend you use JAGS instead, but that is not in the answer key unfortunately so if you are an autodidact that makes this difficult. In November 2014 a second edition that uses JAGS instead, as well as adding in STAN, is coming out, so if you are reading this prior to Nov. 2014 hold out for the second edition, or if you are reading this AFTER Nov. 2014, be aware of the second edition!

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Doing Bayesian Data Analysis: A Tutorial with R and BUGS PDF

Doing Bayesian Data Analysis: A Tutorial with R and BUGS PDF