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 Bug fixes. 1) Some out-of-date versions of the copyright notice were replaced with the current version. 2) Fixed a bug in dist-est, in which using too many iterations produced garbage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).