Neural Classifiers based Monte Carlo simulation

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 labellized samples produced by a mixture density model, i.e., a convex linear combination of two pdfs, and can thus be used for approximating the ratio of these two densities. This bridge between simulation and classification techniques enables us to propose (approximate) pdf-ratios-based simulation algorithms which are built only from a labellized training data set.