A maximum-entropy (exponential-form) model on a large sample space.
The model expectations are not computed exactly (by summing or integrating over a sample space) but approximately (by Monte Carlo estimation). Approximation is necessary when the sample space is too large to sum or integrate over in practice, like a continuous sample space in more than about 4 dimensions or a large discrete space like all possible sentences in a natural language.
Approximating the expectations by sampling requires an instrumental distribution that should be close to the model for fast convergence. The tails should be fatter than the model.
Methods
beginlogging | |
clearcache | |
crossentropy | |
dual | |
endlogging | |
entropydual | |
estimate | |
expectations | |
fit | |
grad | |
log | |
lognormconst | |
logparams | |
logpdf | |
normconst | |
pdf_function | |
resample | |
reset | |
setcallback | |
setparams | |
setsampleFgen | |
setsmooth | |
settestsamples | |
stochapprox | |
test([label, verbose, extra_argv, doctests, ...]) | Run tests for module using nose. |