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Bayesian Methods for Data Analysis
Broadening its scope to nonstatisticians, Bayesian Methods for Data Analysis, Third Edition provides an accessible introduction to the foundations and applications of Bayesian analysis. Along with a complete reorganization of the material, this edition concentrates more on hierarchical Bayesian modeling as implemented via Markov chain Monte Carlo (MCMC) methods and related data analytic techniques. New to the Third Edition Ideal for Anyone Performing Statistical Analyses Focusing on applications from biostatistics, epidemiology, and medicine, this text builds on the popularity of its predecessors by making it suitable for even more practitioners and students.
552 pages, Hardcover
First published July 2, 2008
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December 23, 2010
I used this with two students to serve as my introduction to Bayesian methods. One was a good programmer, strong in mathematics but perhaps weak on statistics. The other was a PhD student in statistics, but had no background in Bayesian methods or programming. I am a decent programmer, decent in statistics, no background in Bayesian methods.
We found the book to be technically challenging. Since this was our intent in choosing the book that is a plus. I did many of the exercises and they complemented the book well. (Note: I used JAGS instead of WinBUGS. Everything works with only occasional changes due to slightly different syntax in the BUGS models) I had to write the author regarding one of the case studies because of an error, and he was quite responsive.
At the end of the day, we understood Bayesian methods enough to tackle a major project. And we have demonstrated that the results of our method leads to accurate statements of probability when compared against test data. And since being able to make accurate statements about probability was the aim of this whole exercise, I have to say that using this book was a success.
The one caveat it helps to have some level of competence in both statistics and programming to use this book. I got considerably more out of it then either of my students. I have recommended that my statistics student get the Gelman et al Bayesian Data Analysis to be a complement to this as it approaches the subject from a different angle that may be more understandable until her programming skills improve.
We found the book to be technically challenging. Since this was our intent in choosing the book that is a plus. I did many of the exercises and they complemented the book well. (Note: I used JAGS instead of WinBUGS. Everything works with only occasional changes due to slightly different syntax in the BUGS models) I had to write the author regarding one of the case studies because of an error, and he was quite responsive.
At the end of the day, we understood Bayesian methods enough to tackle a major project. And we have demonstrated that the results of our method leads to accurate statements of probability when compared against test data. And since being able to make accurate statements about probability was the aim of this whole exercise, I have to say that using this book was a success.
The one caveat it helps to have some level of competence in both statistics and programming to use this book. I got considerably more out of it then either of my students. I have recommended that my statistics student get the Gelman et al Bayesian Data Analysis to be a complement to this as it approaches the subject from a different angle that may be more understandable until her programming skills improve.
Displaying 1 of 1 review