Visitors are encouraged to download my course notes on Bayesian
inference and Markov chain Monte Carlo. I taught my first PhD course
on Bayesian inference in 1985, and have updated and revised my notes over
the years. They also form the basis of workshops that I have
presented to professional statisticians and marketing researchers.
Many researchers have found them to be a good reference for mathematical
details.

The notes are split between pdfs. The first file, in addition to
summarizing the highlights of Bayesian theory, develops the distribution
theory and matrix algebra to analyze some standard models, such as
hierarchical Bayes multivariate regression, logit and probit models.
The notes are in "pseudo" book style and walks through the algebra of
Bayesian inference. The pdf file is very large and takes a few
moments to download. Also, the first two pages are blank - a feature
of Tex's book document class.

Bayes Notes

I have implemented these procedures in GAUSS. The second pdf
contains the GAUSS code for these procedures, both to simulate data from
the model and to analyze the data via MCMC. Students of Bayesian
inference and WinBugs users may be interested in the code to better
understand the algorithms. A third resource is a zip file of the
code.

Program Notes

GAUSS Code