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Regenerative Markov Chain Importance Sampling
Authors:Andrew L Nguyen
Institution:Department of Mathematics, California State University, Fullerton, California, USA
Abstract:We introduce Markov Chain Importance Sampling (MCIS), which combines importance sampling (IS) and Markov Chain Monte Carlo (MCMC) to estimate some characteristics of a non-normalized multi-dimensional distribution. Especially, we introduce some importance functions whose variates are regeneratively generated by MCMC; these variates then are used to estimate the quantity of interest through IS. Because MCIS is regenerative, it overcomes the burn-in problem associated with MCMC. It could also speed up the mixing rate in MCMC.
Keywords:Importance Sampling  MCMC  Regenerative
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