                                     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-dft-a.doc         Building an additive model using two diffusion trees

    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