MIX-PRED: Make predictions for test cases using mixture models. Mix-pred prints the log of the predictive joint probability (or density) for the target values in each test case given. Target values and average log probabilities can be displayed as well. Usage: mix-pred options { log-file range } [ / test-inputs [ test-targets ] ] The final optional arguments give the source of inputs and targets for the cases to look at; they default to the test data specification in the first log file given. The mixture models to use for the predictions are taken from the records with the given ranges of indexes in the given log files. The predictive probabilities are the mixture of these mixtures. An index range can have one of the forms "[low][:[high]][%mod]" or "[low][:[high]]+num", or one of these forms preceded by "@". When "@" is present, "low" and "high" are given in terms of cpu time, otherwise they are iteration numbers. When just "low" is given, only that index is used. If the colon is included, but "high" is not, the range extends to the highest index in the log file. The "mod" form allows iterations to be selected whose numbers are multiples of "mod", with the default being "mod" of one. The "num" form allows the total number of iterations used to be specified; they are distributed as evenly as possible within the specified range. Note that it is possible that the number of mixture models used in the end may not equal this number, if records with some indexes are missing. The 'options' argument consists of one or more of the following letters: t Display the target values for each case. r Use the raw form of the target values, before transformation. p Display the log of the joint probability (or probability density) of the true targets. Logs are to base e. b Suppress headings and averages - just bare numbers for each case. The numbers are printed in exponential format, to high precision. a Display only the average log probability (density), suppressing the results for individual cases. (Makes sense only if 'p' is specified as well, but not 't'.) For iterations in which not all components are represented explicitly (which will always be the case for infinite mixtures), the probability (density) with respect to a new component is approximated by randomly picking a set of 10 parameter values for the new component. These Monte Carlo estimates are found using a random number stream initialized by setting the seed to one at the start of the program. Accuracy can be increased by repeating the same log-file/range combination several times, effectively increasing the sample size use. The average log probability figure is accompanied by +- its standard error (as long as there is more than one test case). If only targets are to be displayed (no predictions), one may give just a single log file with no range. Otherwise, at least one mixture model must be specified. Copyright (c) 1995-2004 by Radford M. Neal