    NOTES ON THE VERSION OF 1997-07-22:

    Changes in this version:

    1) Programs for finite and infinite mixture models have been added.
       The facilities provided are currently somewhat minimal, but are
       adequate for doing some interesting things.  Two new examples
       demonstrating the mixture models have been added.

    2) A program for training networks by old-fashioned gradient descent
       has been added, to allow experimental comparisons.  See net-gd.doc
       for details.  A program useful for doing early stopping using
       cross-validation has also been added (see find-min.doc).

    3) The exponential parts of a Gaussian process specification can now
       include "delta" and "omit" options.  The delta option is useful
       for categorical inputs.  The omit option allows one to explicitly
       set up additive models.  See gp-spec.doc for details.

    4) One can now specify that the noise in a Gaussian process regression
       model is autocorrelated (for training cases).  See model-spec.doc
       for details.

    5) A new "M" quantity has been provided for Gaussian process models,
       which summarizes the magnitude of an input's relevance.  See
       gp-quantities.doc for details.

    6) A log-append program has been added, which allows a run to be
       initialized with a state from another run, provided the other
       run is compatible with the new in all required respects.

    7) A gp-cov program has been added that prints the covariance matrix
       for a Gaussian process.  A gp-eigen program has also been added,
       which computes the eigenvalues and/or eigenvectors of this
       covariance matrix.

    8) Routines have been added to util/matrix.c for initializing an
       identity matrix and finding eigenvalues and eigenvectors by Jacobi
       iteration.

    9) The output of net-pred and gp-pred when the 'b' or 'B' option
       is used has been changed so that the numbers are printed to high
       precision, in exponential format.

    10)The accuracy of the Monte Carlo procedure for making predictions
       for binary targets with Gaussian process models has been improved
       somewhat, using post-stratification.

    11)The stepsize heuristics for Gaussian processes have been changed
       slightly.  The stepsizes for the noise hyperparameters are now
       half what they were, which seems to be a closer fit to the
       typical situation.  It may now be possible to increase the stepsize
       adjustment factor used, and thereby obtain more efficient exploration
       of the hyperparameter space.

    12)The stepsize heuristics for neural networks with more than one
       hidden layer have also been changed, by fixing a bug.  The hope
       is that this change will also be an improvement, without the need
       for a change in the stepsize adjustment factor used, but this
       might not always be true.

    Bug fixes: Fixed a bug that made the "radial heatbath" Markov chain
    method sometimes not work.  Fixed a bug in net-eval that affected it
    when the "targets" option was used.  Fixed the bug in network stepsizes
    mentioned in (10) above.  Some bugs in error checking fixed.

    Portability: The modes for "fopen" have been changed, since the old
    ones didn't work on some systems.  Some other changes that seem
    advisable but which probably weren't actually causing problems have
    been made as well.

    As far as I know, log files created with the previous versions back
    to that of 1996-08-26 should still be readable with the new version,
    on the same machine as they were created on.

    Known bugs and other problems.

    1) The facility for plotting quantities using "plot" operations in xxx-mc
       doesn't always work for the first run of xxx-mc (before any
       iterations exist in the log file).  A work-around is to do a run of
       xxx-mc to produce just one iteration before attempting a run of
       xxx-mc that does any "plot" operations.

    2) The CPU time features (eg, the "k" quantity) will not work correctly
       if a single iteration takes more than about 71 minutes.

    3) The "sample-values" and "scan-values" operations for Gaussian processes
       always recompute the inverse covariance matrix, even when an up-to-date
       version was computed for the previous Monte Carlo operation.