  Acceptance-rejection (AR), Independent Metropolis Hastings (IMH) or
importance sampling (IS) Monte Carlo (MC) simulation algorithms all involve
computing ratios of probability density functions (pdfs). On the other hand,
classifiers discriminate labeled samples produced by a mixture of two
distributions and can be used for approximating the ratio of the two
corresponding pdfs.This bridge between simulation and classification enables us
to propose pdf-free versions of pdf-ratio-based simulation algorithms, where
the ratio is replaced by a surrogate function computed via a classifier. From a
probabilistic modeling perspective, our procedure involves a structured energy
based model which can easily be trained and is compatible with the classical
samplers.
