Software & Technical Report available

[Radford Neal]

    Now available free for research and educational use:

          SOFTWARE FOR FLEXIBLE BAYESIAN MODELING 

This software implements a variety of Bayesian models for 
regression and classification based on neural networks and 
Gaussian processes.  The software is written in C for Unix.

The neural network programs are an update of those previously
distributed, which are described in my book, Bayesian Learning 
for Neural Networks (Springer-Verlag 1996, ISBN 0-387-94724-8).

The Gaussian process models and their implementation are 
described in the following technical report:


   MONTE CARLO IMPLEMENTATION OF GAUSSIAN PROCESS MODELS FOR

          BAYESIAN REGRESSION AND CLASSIFICATION

                      Radford M. Neal
      Dept. of Statistics and Dept. of Computer Science
                   University of Toronto

Gaussian processes are a natural way of defining prior distributions
over functions of one or more input variables.  In a simple non-
parametric regression problem, where such a function gives the 
mean of a Gaussian distribution for an observed response, a Gaussian
process model can easily be implemented using matrix computations 
that are feasible for datasets of up to about a thousand cases.
Hyperparameters that define the covariance function of the Gaussian
process can be sampled using Markov chain methods.  Regression 
models where the noise has a t distribution and logistic or probit 
models for classification applications can be implemented by sampling 
as well for latent values underlying the observations.  Software is 
now available that implements these methods using covariance 
functions with hierarchical parameterizations.  Models defined in 
this way can discover high-level properties of the data, such as 
which inputs are relevant to predicting the response.


Both the software and the technical report can be obtained via my
home page, at URL

   http://www.cs.utoronto.ca/~radford/

You can directly obtain the compressed Postscript for the technical 
report at URL

   ftp://ftp.cs.utoronto.ca/pub/radford/mc-gp.ps.Z


Please let me know if you encounter any difficulties.

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Radford M. Neal                                       radford at cs.utoronto.ca
Dept. of Statistics and Dept. of Computer Science radford at utstat.utoronto.ca
University of Toronto                     http://www.cs.utoronto.ca/~radford
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