Learning when to reject an importance sample
Jeremy C. Weiss, Sriraam Natarajan, C. David Page · 2013
When observations are incomplete or data are missing, ap-proximate inference methods based on importance sampling are often used. Unfortunately, when the target and proposal distributions are dissimilar, the sampling procedure leads to biased estimates or requires a prohibitive number of sam-ples. Our method approximates a multivariate target distri-bution by sampling from an existing, sequential importance sampler and accepting or rejecting the proposals. We develop the rejection-sampler framework and show we can learn the acceptance probabilities from local samples. In a continuous-time domain, we show our method improves upon previous importance samplers by transforming a sequential importance sampling problem into a machine learning one.