Learning When to Reject an Importance Sample

When observations are incomplete or data are missing, approximate 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 samples. Our method approximates a multivariate target distribution 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.