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