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. ----------------------------------------------------------------------- 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.