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Markov chain importance sampling with applications to rare event probability estimation |
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| Authors: | Zdravko I Botev Pierre L’Ecuyer Bruno Tuffin |
| Institution: | 1. School of Mathematics and Statistics, University of New South Wales, Sydney, NSW, 2052, Australia 2. Department of Computer Science and Operations Research, Université de Montréal, Pavillon André-Aisenstadt CP 6128 succ Centre-Ville, Montréal, QC, H3C 3J7, Canada 3. INRIA Rennes Bretagne-Atlantique, Campus de Beaulieu, 35042, Rennes Cedex, France |
| Abstract: | We present a versatile Monte Carlo method for estimating multidimensional integrals, with applications to rare-event probability estimation. The method fuses two distinct and popular Monte Carlo simulation methods—Markov chain Monte Carlo and importance sampling—into a single algorithm. We show that for some applied numerical examples the proposed Markov Chain importance sampling algorithm performs better than methods based solely on importance sampling or MCMC. |
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