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 common aspects of
commands) contained in the files ending with ".doc" which are found in
the various sub-directories, and also all collected in 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 documenation. It can
be accessed using a Web browser (eg, xmosaic or netscape), by a
command such as
> xmosaic index.html
or by opening index.html as a local file in a browser that you are
already running. The index.html file must reside in the 'doc'
directory for this software. One needn't start the browser from this
directory, however, provided one uses a full path name to identify
index.html. 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.
All the introductory documentation (including this) is collected into
the 'manual' file. 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 net/*.doc gp/*.doc mix/*.doc doc/rel*
(or using whatever command other than 'lpr' you use to print text files).
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
* data-spec Specify data sets for training and testing
* numin Facilities for input of numeric data
numin-test Test numeric input module
* 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
rand-test Test random number generators
* quantities Numeric quantities obtainable from log files
* xxx-plt Write quantities from a log file, suitable for plotting
xxx-hist Build a histogram for a quantity obtained from a log file
* 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 Markov chain simulation
* 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
xxx-grad-test Test the correctness of the energy gradient computations
xxx-stepsizes Display and evaluate stepsizes used for dynamics
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-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 a net log file, suitable for plotting
net-hist Build histogram for quantity obtained from a net log file
* 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-cases Generate cases from a mixture model
mix-extensions Possible extensions to the mixture modeling software
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-mc Do Markov chain simulation for a bivariate Gaussian
bvg-plt Get quantities from a bvg log file, suitable to plot