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Data Analysis: A Bayesian Tutorial by Devinderjit Sivia, John Skilling

Data Analysis: A Bayesian Tutorial Devinderjit Sivia, John Skilling ebook
Page: 259
Publisher: Oxford University Press, USA
Format: pdf
ISBN: 0198568320, 9780198568322

@Luis, I think Data Analysis: A Bayesian Tutorial (http://www.amazon.com/Data-Analysis-Bayesian-Tutorial-Publications/dp/0198518897) is just such a book. Hierarchical Bayesian estimation is a complex but powerful approach of modeling data sets to yield more precise and granular analysis. We will use the data set survey for our first demonstration of OpenBUGS. 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. Our lab uses Mathematica quite a bit for data analysis and building models. Simon Jackman's Bayesian Analysis for the Social Sciences. "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. There aren't that many other people in psychology at NYU (or elsewhere) that use Mathematica. 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. Jaynes; Introduction to Bayesian statistics, William M. Tutorials will last 100 minutes. {This is the first book on the maximum entropy and Bayesian methods aimed at senior undergraduates in science and engineering. Probability Theory: The Logic of Science, E. Perform Markov Chain Monte Carlo convergence analysis using CODA. Stan has all the generality and ease of use of BUGS, and can solve the multilevel generalized linear models described in Part II of the book Data Analysis Using Regression and Multilevel/Hierarchical Models. Tutorial on Bayesian inference using OpenBUGS. Bolstad; Doing Bayesian Data Analysis: A Tutorial with R and BUGS, John K. The conference aims at bringing together researchers and practitioners to discuss recent developments in computational methods, methodology for data analysis and applications in statistics.