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