FACILITIES PROVIDED BY THIS SOFTWARE 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. These facilities might be useful for actual problems, but you should note that many features that might be needed for real problems have not been implemented, that the programs have not been tested to the extent that would be desirable for important applications, and that permission to use the software for free is granted only for purposes of research and education. The complete source code (in C) is provided, allowing researchers to modify the program to test new ideas. It is not necessary to know C to use the programs (assuming you manage to install them correctly). This software is designed for use on a Unix or Linux system, using commands issued to the command interpreter (shell). No particular window system or other GUI is required, but a plotting program will be very useful. I use the xgraph plot program, written by David Harrison, which allows plots to be produced by just piping data from one of the commands; it can be obtained from my web page. Markov chain Monte Carlo facilities. All the Bayesian models are implemented using Markov chains to sample from the posterior distribution. For the elaborate models based on neural networks, Gaussian processes, and mixtures, this is done by combining general-purpose Markov chain sampling procedures with special modules written in C. Other models could be implemented in the same way, but this is a fairly major project. To allow people to play around with the various Markov chain methods more easily, a facility is provided for defining distributions (on R^n) by giving a simple formula for the probability density. Many Markov chain sampling methods, such as the Metropolis algorithm, hybrid Monte Carlo, slice sampling, and simulated tempering, may then be used to sample from this distribution. Bayesian posterior distributions can be defined by giving a formula for the prior density and for the likelihood based on each of the cases (which are assumed to be independent). A long review paper of mine on "Probabilistic Inference Using Markov Chain Monte Carlo Methods" can be obtained from my web page. This review discusses methods based on Hamiltonian dynamics, including the "hybrid Monte Carlo" method. These methods are also discussed in my book on "Bayesian Learning for Neural Networks". My web page also has papers on slice sampling ("Markov chain Monte Carlo methods based on `slicing' the density function" and "Slice sampling"), Annealed Importance Sampling, and circularly-coupled Markov chain sampling, all of which are implemented in this software. Neural network and Gaussian process models. The neural network models are described in my thesis, "Bayesian Learning for Neural Networks", which has now been published by Springer-Verlag (ISBN 0-387-94724-8). The neural network models implemented are essentially as described in the Appendix of this book. The Gaussian process models are in many ways analogous to the network models. The Gaussian process models implemented in this software, and computational methods that used, are described in my technical report entitled "Monte Carlo implementation of Gaussian process models for Bayesian regression and classification", available from my web page, and in my Valencia conference paper on "Regression and classification using Gaussian process priors", in Bayesian Statistics 6. The Gaussian process models for regression are similar to those evaluated by Carl Rasmussen in his thesis, "Evaluation of Gaussian Processes and other Methods for Non-Linear Regression", available from his web page, at the URL http://www.cs.utoronto.ca/~carl/; he also talks about neural network models. To understand how to use the software implementing these models, it is essential for you to have read at least one of these references. The neural network software supports Bayesian learning for regression problems, classification problems, and survival analysis, using models based on networks with any number of hidden layers, with a wide variety of prior distributions for network parameters and hyperparameters. The Gaussian process software supports regression and classification models that are similar to neural network models with an infinite number of hidden units, using Gaussian priors. The advantages of Bayesian learning for both types of model include the automatic determination of "regularization" hyperparameters, without the need for a validation set, the avoidance of overfitting when using large networks, and the quantification of uncertainty in predictions. The software implements the Automatic Relevance Determination (ARD) approach to handling inputs that may turn out to be irrelevant (developed with David MacKay). For problems and networks of moderate size (eg, 200 training cases, 10 inputs, 20 hidden units), fully training a neural network model (to the point where one can be reasonably sure that the correct Bayesian answer has been found) typically takes up to several hours on a modern personal computer. However, quite good results, competitive with other methods, are often obtained after training for just a few minutes. The time required to train the Gaussian process models depends a lot on the number of training cases. For 100 cases, these models may take only a few minutes to train (again, to the point where one can be reasonably sure that convergence to the correct answer has occurred). For 1000 cases, however, training might well take a day. The software also implements neural network training using early stopping, as described in my paper on "Assessing relevance determination methods using DELVE", in Neural Networks and Machine Learning, C. M. Bishop, editor, Springer-Verlag, 1998. A similar early stopping method is also described in Carl Rasmussen's thesis (see above). Bayesian mixture models. The software implements Bayesian mixture models for multivariate real or binary data, with both finite and countably infinite numbers of components. The countably infinite mixture models are equivalent to Dirichlet process mixture models. The sampling methods that I have implemented for these models are described in my technical report on "Markov chain sampling methods for Dirichlet process mixture models", which can be obtained from web page; see also my technical report on "Bayesian mixture modeling by Monte Carlo simulation". The software implements the basic sampling operations for these models, but is rather preliminary in some respects, lacking many facilities that would be useful in practical applications. Software components. The software consists of a number of programs and modules. Each major component has its own directory, as follows: util Modules and programs of general utility. mc Modules and programs that support sampling using Markov chain Monte Carlo methods, using modules from util. dist Programs for doing Markov chain sampling on a distribution given by a simple formula, or by giving a Bayesian prior and likelihood, using the modules from util and mc. net Modules and programs that implement Bayesian inference for models based on multilayer perceptron neural networks, using the modules from util and mc. Also implements simple gradient descent training, possibly with early stopping. gp Modules and programs that implement Bayesian inference for models based on Gaussian processes, using the modules from util and mc. mix Modules and programs that implement Bayesian inference for finite and infinite mixture models, using modules from util and mc. In addition, the 'bvg' directory contains modules and programs for sampling from a bivariate Gaussian distribution, as a simple demonstration of how the Markov chain Monte Carlo facilities can be used from a special module written in C. Other than by providing this example, and the detailed documentation on various commands, I have not attempted to document how you might go about using the Markov chain Monte Carlo modules for another application written in C. The following directories contain examples of how these programs can be used, many of which are discussed in the documentation: ex-netgp Examples of Bayesian regression and classification models based on neural networks and Gaussian processes. ex-mix Examples of Bayesian mixture models. Includes command files for the test in my paper on "Markov chain sampling methods for Dirichlet process mixture models" ex-dist Examples of Markov chain sampling on distributions specified by simple formulas. ex-bayes Examples of Markov chain sampling for Bayesian models specified using formulas for the prior and likelihood. ex-surv Examples of neural network survival models. ex-gdes Examples of neural network learning using gradient descent and early stopping. ex-ais Contains command and data files used for the tests in my paper on "Annealed importance sampling". You should note, however, that these examples do not constitute "recipes" that can be used unchanged for new problems. They are intended to help you understand the models, priors, and computational methods, so that you can devise an appropriate way of handling whatever problem you are interested in. The 'bin' directory contains links to all the programs. The 'doc' directory contains all the documentation. Portability of the software. The software is written in ANSI C, and is meant to be run in a UNIX environment. It is known to work on various Linux systems running on a Pentium processor, and on an SGI machine running IRIX Release 6.5. It also runs OK on SPARC machine running Solaris, and on Compaq Alpha machines, provided that the -ieee and -std options are given to the Compaq C compiler. As far as I know, the software does not depend on any peculiarities of these environments (except perhaps for the use of the drand48 pseudo-random number generator, and the lgamma function), but you may nevertheless have problems getting it to work in substantially different environments, and I can offer little or no assistance in this regard. There is no dependence on any particular graphics package or graphical user interface. The 'xxx-plt' programs are designed to allow their output to be piped directly into the 'xgraph' plotting program, but other plotting programs can be used instead, or the numbers can be examined directly. The 'xxx-tbl' programs output the same information in a different format, which is useful when plotting or analysing data with S-Plus or R, since this format is convenient for the S-Plus or R read.table command.