GUIDE TO FURTHER DOCUMENTATION The overview and examples above are intended just to get you started. To use the software to do real work, you will probably need to refer to the detailed documentation on the commands (and on the features common to more than one command) that is contained in the files ending with ".doc". These files are found in the various sub-directories, and are also all linked to from the 'doc' directory. For quick reference, all commands print a brief summary of the command syntax when they are invoked with no arguments. In the syntax descriptions used, the characters "[" and "]" enclose parts of the command that are optional, "{" and "}" enclose optional parts that can be repeated, and "|" separates alternatives. Except for the command name (or other obvious keywords), the words in the syntax descriptions are descriptive of what is to be entered, except that words in quotes are to be entered literally (without the quotes). The ".doc" files present in the various directories are listed below, with the more important files marked by "*". Programs listed as "xxx-something" are generic, with "xxx" being replaced by the name of an application (eg, "net", "gp", or "mix"). In some cases, further documentation is available under the specific name. The file index.html is a hypertext index to this documentation. It can be accessed using a Web browser, by opening index.html as a local file (which will probably require giving its full path name). The index.html file must reside in the 'doc' directory for this software, so that relative references will work correctly. The files accessed this way are .html files derived from the .doc files. The content is identical, except that references to other .doc files have been converted into hypertext links that you can follow with the browser. The 'doc' directory also contains comments on the various software releases, in files of the form 'Release.YYYY-MM-DD.doc'. Generic utility programs [util]: * 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 Markov chain Monte Carlo facilities [mc]: * 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 Markov chain sampling for a specified distribution [dist]: * 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 Markov chain sampling for a bivariate Gaussian [bvg]: 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 Bayesian neural networks [net]: * net Bayesian inference for neural networks using MCMC * net-spec Create a new network, or display existing specifications * net-config Specify weight configuration for a layer's connections * 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-config-check Check/display a network configuration specification * 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 Gaussian process models [gp]: * 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 Bayesian inference for mixture models [mix]: * 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 Bayesian inference for Dirichlet diffusion tree models [dft]: * 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 using Dirichlet diffusion trees dft-cases Generate cases from a diffusion tree model Bayesian inference for locations of sources of atmospheric contamination [src]: * src Programs to infer 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 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. Molecular simulation with Lennard-Jones potential [mol]: * 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