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