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Data Analysis: A Bayesian Tutorial Devinderjit Sivia, John Skilling
Publisher: Oxford University Press, USA

His well commented R-Code can get you into some simple roll-your-own MCMC and Gibbs sampling and his tutorial-like handling of WinBUGS in the raw and through R2WinBUGS is, I think, the best. You can download the book for free here. "Think Stats: Probability and Statistics for Programmers" to help programmers understand and express statistical models, in particular the Bayesian statistics at the heart of many applications. Silver used Bayesian methods to combine the results of various polls, analyze them, and predict the probability of a given candidate winning the majority of votes in a district. Gilks, Richardson, and Spiegelhalter (1996). Bayes and Empirical Bayes Methods for Data Analysis, 2nd Edition. Expensive For theroetical and further readings of MCMC. As a starting point, I'd add Doing Bayesian Data Analysis by John Kruschke and Bayesian Computation with R by Jim Albert to the list. So, how does Bayes' theorem work? Doing Bayesian Data Analysis: A Tutorial with R and BUGS Published: 2010-11-10 | ISBN: 0123814855 | PDF | 672 pages | 10 MB Doing Bayesian Data Analysis: A Tutorial with R and BUGS Publishe. Data Analysis: A Bayesian Tutorial. The key is comparing existing data to a hypothesis: what is the probability of this outcome if a given hypothesis is true? Simon Jackman's Bayesian Analysis for the Social Sciences.