Subject: Software for Bayesian modeling (fwd)

From: Saleh Elmohamed <[email-masked]>
Date: Sun, 9 Aug 1998 16:54:38 -0400 (EDT)

---------- Forwarded message ----------
Date: Sat, 8 Aug 1998 17:06:47 -0400
From: Radford Neal <[email-masked]>
To: [email-masked]
Subject: Software for Bayesian modeling

   FREE SOFTWARE FOR BAYESIAN MODELING USING MARKOV CHAIN MONTE CARLO

A new release of my software for flexible Bayesian modeling is available.  
This software is meant to support research and education regarding:

   * Flexible Bayesian models for regression and classification 
     based on neural networks and Gaussian processes, and for
     probability density estimation using mixtures.  Neural net
     training using early stopping is also supported.

   * Markov chain Monte Carlo methods, and their applications to
     Bayesian modeling, including implementations of Metropolis,
     hybrid Monte Carlo, slice sampling, and tempering methods.

The neural network, Gaussian process, and mixture model facilties are 
updated from the previous version, with some new facilities, and some
bugs fixed.

New programs are provided for applying a variety of Markov chain
methods to distributions that are defined using simple formulas. 
A BUGS-like notation can be used to define some Bayesian models.
Note, however, that I have not attempted to provide comprehensive
facilities for Bayesian modeling.  The intent is more to demonstrate
the Markov chain methods.

One facility that may be of particular interest is that Annealed
Importance Sampling can be used to find the marginal likelihood for a
Bayesian model (though obtaining good results may require some fiddling).

The software is written in C for use on Unix systems.  It is free for
research and educational purposes.  You can get it from my web page.
More directly, you can go to 

   http://www.cs.utoronto.ca/~radford/fbm.software.html

and follow the instruction there.  Or you can just browse the
documentation there to find out more about the software.  There are
also links to various papers that you might want to read before trying
to use the software.

Please let me know if you have any problems with obtaining or
installing the software, or if you have any other comments.

   Radford Neal

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Radford M. Neal                                       [email-masked]
Dept. of Statistics and Dept. of Computer Science [email-masked]
University of Toronto                     http://www.cs.utoronto.ca/~radford
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