Greedy Importance Sampling

Part of Advances in Neural Information Processing Systems 12 (NIPS 1999)

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Dale Schuurmans


I present a simple variation of importance sampling that explicitly search(cid:173) es for important regions in the target distribution. I prove that the tech(cid:173) nique yields unbiased estimates, and show empirically it can reduce the variance of standard Monte Carlo estimators. This is achieved by con(cid:173) centrating samples in more significant regions of the sample space.