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

Simon Jackman's Bayesian Analysis for the Social Sciences. (Consider the example in chapter 1 of Bayesian Data Analysis of empirical probabilities for football point spreads, or the example of kidney cancer rates in chapter 2.) Similarly, subjective . The best intro paper on MDL is probably Grünwald's “A Tutorial Introduction to the Minimum Description Length Principle”, which also addresses your question about priors in MDL (and mentions some consistency results, if I remember correctly). Summary: This book was designed for undergraduates in science and engineering. 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. So, how does Bayes' theorem work? Data Analysis: A Bayesian Tutorial. You can download the book for free here. Title: Bayesian Data Analysis: A Tutorial Authors: D.S. Silva, John Skilling Initially Published: 2006. 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. 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. Hierarchical Bayesian estimation is a complex but powerful approach of modeling data sets to yield more precise and granular analysis. "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. The key is comparing existing data to a hypothesis: what is the probability of this outcome if a given hypothesis is true?