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Rating: 
Summary: Finally!
Review: Finally a book that explains Bayesian stats in social science terms. Why should we care? How do we use this stuff? What does it do that couldn't be done otherwise? The examples are great because they use actual data that can be downloaded and run with supplied code. The theory is carefully explained and doesn't assume that readers have had years of math-stat. I particularly liked the introduction to MCMC tools.
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Summary: making Bayes applicable
Review: Jeff Gill is a statistician and a programming geek. He writes code in R and S. This book is an introduction to Bayesian methods for social scientists with the primary goal of making Bayesian methods accessible and used in that discipline. I discovered Jeff when I took a course from George Casella on Markov Chain Monte Carlo (MCMC). Jeff helped George teach the ins and outs of BUGS and BOA and CODA all common and important tools for the implementation and understanding of MCMC. In the course Jeff presented material from examples in this book (which was not yet out when I took the course). I knew then that I wanted to get this book first chance I got!I am a statistician and this is a great reference for statisticians and biostatisticians who are also finding Bayesian methods and MCMC very useful. The book is designed for social scientists but is good for everyone wanting to do sophisticated Bayesian analyses!
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Summary: winbugs, etc.
Review: this book is really helpful if you're learning winbugs and mcmc for the first time, and handy even if you're not new. There are lots of examples, with code and explanation.
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Summary: disappointing
Review: This book is turgid, filled with minor & major errors, and generally useless--a real disappointment from Chapman & Hall/CRC which usually maintains high standards. I'm reading Gelman et al.'s Bayesian Data Analysis which is much superior.
Rating:
Summary: Absolutely Fabulous
Review: This is a incredibly well-presented introduction to Bayesian methods and Bayesian posterior simulation. Gill goes from the bare-bones introduction through Gibbs, Metropolis, and annealing. Every chapter has a set of examples with data, code, and interesting results. The technical level is spot-on: detailed but not overwhelming. As an industry practitioner (finance) rather than someone at university, this book has been very helpful in getting me started in this area. What I've found this summer is that this book leads nicely into more advanced work which I've been exploring: the Congdon book, Carlin and Louis, and even Chen, Shao, and Ibrahim. I'm also new to winbugs and R, having been a SAS user for quite some time, and the worked examples in the Gill book are quite helpful in that one can run them immediately with code supplied. Superbly one does not even have to type them in as they are supplied on the net (along with some software links, errata, and other tidbits). My only real complaint here is that I would have liked to see an extended section on empirical Bayes. But as this is featured in Carlin and Louis, its not too large of an issue. If you're interested in Bayesian statistics, this is a "must-own". While there seems to be plenty of high-level works out there, particularly in say biostats, there are relatively few that get you started and provide such extensive detail.
Rating:
Summary: winbugs, etc.
Review: This is an extremely good way to become familiar with Bayesian methods. Most books don't help you through things to the degree that this one does. It starts with really basic principles necesary to understand Bayesian principles and goes all the way through MCMC techniques like Gibbs Sampling and simulated annealing. I especially like the way many of the intermediate steps are included rather than assuming the reader has the time to work through all of them. A nice addition to my methodology bookshelf.
Rating:
Summary: Very Helpful
Review: This is an extremely good way to become familiar with Bayesian methods. Most books don't help you through things to the degree that this one does. It starts with really basic principles necesary to understand Bayesian principles and goes all the way through MCMC techniques like Gibbs Sampling and simulated annealing. I especially like the way many of the intermediate steps are included rather than assuming the reader has the time to work through all of them. A nice addition to my methodology bookshelf.
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