    NOTES ON THE VERSION OF 2004-11-10

    Except for the lightly-documented addition of Hamiltonian importance
    sampling, this release contains only some fairly minor (though useful)
    extensions and some bug fixes.

    Changes in this version:

    1) Hamiltonian importance sampling was added.  See xxx-his.doc.  More
       tutorial documentation (and maybe a paper!) may be written some day.

    2) A "count" data model has been added, which models Poisson count
       data.  It is implemented only for Gaussian process models, where
       the Gaussian process produces the log of the Poisson mean.

    3) The "numin" module has been changed to allow for missing values,
       written as "?", which are allowed for targets by some models (currently
       only neural network models).

    4) The gp-eval program has been extended to allow evaluations at points
       read from a data file, rather than from a grid.  See gp-eval.doc for
       details.

    5) An "E" option has been added to net-pred and gp-pred, which gives the
       expected fraction of wrong guesses at binary targets.  For details,
       see net-pred.doc and gp-pred.doc.  This was documented in the last
       release, but not actually included in the version of the program
       that was distributed.

    6) Data file reading was changed to not read the whole data file if only
       a part of it is required.  The speed of reading when there are many
       inputs or targets has also been improved.

    7) A new "K0" quantity has been added, equal to the expected value of
       the kinetic energy (the "K" quantity).  See mc-quantities.doc.

    Bug fixes.

    1) Some out-of-date versions of the copyright notice were replaced with the
       current version.

    2) Fixed a bug in dist-est, in which using too many iterations produced
       garbage results, rather than an error message.

    3) Fixed a bug that affected "offsets" for neural network models.

    4) Fixed some other bugs involving error checking.

    Known bugs and other deficiencies.

    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 latent value update operations for Gaussian processes may recompute
       the inverse covariance matrix even when an up-to-date version was
       computed for the previous Monte Carlo operation.

    4) Covariance matrices are stored in full, even though they are symmetric,
       which sometimes costs a factor of two in memory usage.

    5) Giving net-pred several log files that have different network architectures
       doesn't work, but an error message is not always produced (the results may
       just be nonsense).