README

  FLEXIBLE BAYESIAN MODELLING SOFTWARE, VERSION OF 2020-01-24

  This directory and its subdirectories contain software for flexible
  Bayesian learning of regression, classification, density, and other
  models, based on multilayer perceptron neural networks, Gaussian
  processes, finite and countably infinite mixtures, and Dirichlet
  diffusion trees, as well as facilities for inferring sources of
  atmospheric contamination and for molecular simulation.  These are
  implemented using Markov chain Monte Carlo methods.  Facilities for
  Markov chain sampling from distributions specified by simple
  formulas for the density or for the prior and likelihood are also
  included.

  For more information, see the files in the 'doc' directory.   The
  file 'index.html' in 'doc' has links to all the documentation, which
  is easily perused with a web browser.  Note: You must access 'index.html' 
  in the 'doc' directory, not copy it somewhere else, since the links are 
  relative.

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  The contents of this directory and its sub-directories are 
  Copyright (c) 1995-2020 by Radford M. Neal
  
  Permission is granted for anyone to copy, use, modify, or distribute these
  programs and accompanying documents for any purpose, provided this copyright
  notice is retained and prominently displayed, along with a note saying 
  that the original programs are available from Radford Neal's web page, and 
  note is made of any changes made to these programs.  These programs and 
  documents are distributed without any warranty, express or implied.  As the
  programs were written for research purposes only, they have not been tested 
  to the degree that would be advisable in any important application.  All use
  of these programs is entirely at the user's own risk.