Copyright (c) 1995-2020 by Radford M. Neal

Index to documentation on software for various Markov chain Monte Carlo methods and their use for various flexible Bayesian models as well as molecular simulation.

- Copyright and contact information
- Facilities provided by this software
- Installing the software
- Overview of the software
- Introduction to the examples
- Examples of Markov chain sampling for simple distributions
- Examples of circularly-coupled Markov chain sampling
- Examples of Markov chain sampling for simple Bayesian models
- Examples of flexible Bayesian regression and classification models based on neural networks and Gaussian processes
- Examples of Bayesian mixture models and Dirichlet diffusion tree models
- Example of classification with a Dirichlet diffusion tree joint model
- Examples of Bayesian neural network survival models
- Examples of learning with gradient descent, early stopping & ensembles
- Examples of inference for locations of sources of atmospheric contaminants
- Hints and warnings
- Guide to further documentation
- Acknowledgements

- Release of 2020-01-24
- Release of 2007-03-31
- Release of 2004-11-10
- Release of 2003-06-29
- Release of 2001-08-31
- Release of 2000-08-21
- Release of 1999-12-06
- Release of 1999-03-13
- Release of 1998-09-01
- Release of 1998-08-02
- Release of 1997-07-22
- Release of 1997-01-18
- Release of 1996-08-26
- Release of 1995-08-09

........ UTIL ......... ..... MC ..... .... DIST ..... ... BVG ... calc mean mc dist bvg data-spec model-spec mc-ais dist-dgen bvg-initial extract numin mc-quantities dist-display bvg-mc find-min prior mc-spec dist-est bvg-plt formula quantities mc-temp-filter dist-gen bvg-spec grid rand-seed mc-temp-sched dist-initial log series xxx-circ dist-mc log-append xxx-hist xxx-genp dist-quantities log-copy xxx-plt xxx-grad-test dist-spec log-equal xxx-tbl xxx-his dist-stepsizes log-last xxx-mc log-records xxx-mc-test log-types xxx-stepsizes xxx-wrap .... NET ..... .... GP ..... .... MIX ..... .... DFT .... net gp mix dft net-approx gp-cov mix-cases dft-cases net-dvar gp-dgen mix-display dft-dendrogram net-display gp-display mix-gen dft-display net-eval gp-eigen mix-mc dft-gen net-gd gp-eval mix-pred dft-mc net-gen gp-gen mix-quantities dft-pred net-hist gp-mc mix-spec dft-quantities net-mc gp-pred dft-spec net-plt gp-quantities net-pred gp-spec net-quantities net-rej net-spec net-tbl .... SRC .... .... MOL ..... src mol src-dgen mol-display src-display mol-mc src-gen mol-quantities src-initial mol-spec src-intensity src-mc src-pred src-quantities src-spec det-spec flow-specA more detailed index is given below, with the most important documentation files marked with an asterisk.

* log Facilities for handling log files log-types Types of log file records used by various programs log-copy Copy part of a log file to a new log file log-append Append records from one log file to the end of another log-last Display the index of the last record in a log file log-records List all records in a log file log-equal Check if records in log files match formula Syntax for arithmetic formulas calc Simple calculator program * data-spec Specify data sets for training and testing * numin Facilities for input of numeric data * model-spec Specify model for targets * prior Meaning and syntax of prior specifications find-min Find entry with minimum value (for cross validation) grid Output a grid of points extract Extract items at random from a data file * rand-seed Specify a random number seed * quantities Numeric quantities obtainable from log files * xxx-plt Write quantities from log files, suitable for plotting xxx-tbl Write quantities from log files in a tabular form xxx-hist Build a histogram for a quantity using data from log files * series Analyse stationary time series data * mean Compute means with standard errors

* mc Programs and modules supporting Markov chain Monte Carlo * mc-spec Specify how to do the Markov chain simulation * xxx-mc Run a Markov chain simulation * xxx-circ Do a circularly-coupled simulation * xxx-wrap Create wrapped-around chain from existing simulation run * mc-quantities Quantities from log files relating to Monte Carlo mc-temp-sched Specify temperature schedule for tempering methods mc-temp-filter Copy only iterations at a given temperature mc-ais Monitor annealed importance sampling (AIS) runs xxx-mc-test Do a joint distribution test of MCMC correctness xxx-grad-test Test the correctness of the energy gradient computations xxx-stepsizes Display and evaluate stepsizes used for dynamics xxx-genp Generate random momentum variables xxx-his Do Hamiltonian importance sampling

* dist Markov chain sampling for a specified distribution * dist-spec Specify a distribution to sample from dist-initial Specify initial state for Markov chain dist-stepsizes Display, evaluate, or set stepsizes used for dynamics * dist-mc Do Markov chain sampling for the specified distribution dist-gen Generate values for state variables from the prior dist-dgen Generate values for target variables using given parameters dist-display Print state variables at a specified iteration * dist-quantities Quantities defined for a specified distribution * dist-est Estimate the expectation of some function of state

bvg Demo of Markov chain sampling from a bivariate Gaussian bvg-spec Specify a bivariate Gaussian distribution to sample from bvg-initial Set initial state for sampling from a bivariate Gaussian bvg-mc Do Markov chain simulation for a bivariate Gaussian bvg-plt Get quantities from a bvg log file, suitable to plot

* net Bayesian inference for neural networks using MCMC * net-spec Create a new network, or display existing specifications * net-mc Do Markov chain simulation to sample networks * net-gen Generate networks from the prior, or with fixed values net-approx Specify quadratic approximation to replace log likelihood net-gd Train a network by gradient descent in the error * net-display Print network parameters and/or hyperparameters * net-quantities Quantities from log files relating to networks * net-plt Get quantities from net log files, suitable for plotting net-tbl Get quantities from net log files and output as table net-hist Build histogram for quantity obtained from net log files * net-pred Make predictions for test cases net-eval Evaluate network functions over a grid net-dvar Find the variance of a difference in function values net-rej Generate networks from the posterior by rejection sampling

* gp Bayesian modelling using Gaussian processes * gp-spec Specify a Gaussian process model, or display existing spec * gp-mc Use Markov chain to sample Gaussian process hyperparameters * gp-gen Generate GP hyperparameters randomly, or fix them gp-dgen Generate values for target variables given latent values * gp-display Print Gaussian process hyperparameters & other information * gp-quantities Quantities from log files relating to Gaussian processes * gp-pred Make predictions for test cases using Gaussian process gp-eval Evaluate function drawn from a Gaussian process over a grid gp-cov Print covariance matrix for a Gaussian process gp-eigen Find eigenvalues/vectors of covariance matrix

* mix Bayesian inference for mixture models * mix-spec Specify a mixture model, or display existing spec * mix-mc Use Markov chain to do sampling for a mixture model * mix-gen Generate hyperparameters randomly, or fix them * mix-display Print mixture model parameters, hyperparameters, etc. * mix-quantities Quantities from log files relating to mixture models * mix-pred Make predictions for tests cases using mixture models mix-cases Generate cases from a mixture model

* dft Bayesian inference for diffusion tree models * dft-spec Specify a diffusion tree model, or display existing spec * dft-mc Use Markov chain to do sampling for a diffusion tree model * dft-gen Generate hyperparameters randomly, or fix them * dft-display Print diffusion tree model parameters, hyperparameters, etc. dft-dendrogram Create Postscript representation of a dendrogram of a tree * dft-quantities Quantities from log files relating to diffusion tree models * dft-pred Make predictions for test cases using diffusion tree model dft-cases Generate cases from a diffusion tree model

* src Programs for inferring source locations from detector readings * src-spec Specify priors of the number and location of sources * det-spec Specify detector noise model * flow-spec Specify flow model * src-mc Do Markov chain sampling for source location models src-initial Set initial values for parameters of a source location model src-gen Generate randomly from the prior for a source location model src-dgen Randomly generate detector measurements given source parameters * src-display Print the parameters of a source location model * src-quantities Quantities from log files relating to source models * src-pred Make predictions for measurements in test cases * src-intensity Make predictions for source intensity in grid cells.

* mol Molecular simulation with Lennard-Jones potential * mol-spec Specify a molecular system * mol-mc Do Markov chain sampling for a molecular system mol-display Print state of molecular system * mol-quantities Quantities from log files relating to molecular systems