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 (eg, netscape), 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'.

All the introductory documentation (including this) is collected into
the 'manual' file, as plain text.  You can print all the documentation
by going to the main directory for this software and issuing a command
such as

  lpr doc/manual util/*.doc mc/*.doc dist/*.doc bvg/*.doc \
      net/*.doc gp/*.doc mix/*.doc dft/*.doc doc/Rel*.doc
(or using whatever command other than 'lpr' you use to print text files).
This will produce about 200 pages of paper.

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

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-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

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-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-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

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-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