CONTENTS Facilities.doc Facilities provided by this software Install.doc Installing the software Overview.doc Overview of the software Ex-intro.doc Introduction to the examples Ex-dist.doc Examples of Markov chain sampling for simple distributions Ex-dist-n.doc Sampling from a univariate normal distribution Ex-dist-g.doc Sampling from a ring distribution in three dimensions Ex-dist-f.doc Sampling from a funnel distribution in ten dimensions Ex-circ.doc Examples of circularly-coupled Markov chain sampling Ex-bayes.doc Examples of Markov chain sampling for simple Bayesian models Ex-bayes-r.doc A linear regression model Ex-bayes-t.doc Modeling real-valued data with a t-distribution Ex-bayes-p.doc Modeling probabilities for categorical data Ex-bayes-e.doc A random effects model Ex-netgp.doc Examples of flexible Bayesian regression and classification models based on neural networks and Gaussian processes Ex-netgp-r.doc A simple regression problem Ex-netgp-b.doc A problem with a binary response Ex-netgp-c.doc A three-way classification problem Ex-netgp-o.doc A regression problem with outliers Ex-mixdft.doc Examples of mixture models and Dirichlet diffusion trees Ex-mixdft-b.doc A probability estimation problem with binary data Ex-mixdft-r.doc A bivariate density estimation problem Ex-surv.doc Examples of Bayesian neural network survival models Ex-gdes.doc Examples of gradient descent learning with early stopping Hints.doc Hints and warnings Guide.doc Guide to further documentation Acknowledgements.doc