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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Botev, Z.I., L’Ecuyer, P. & Tuffin, B. Markov chain importance sampling with applications to rare event probability estimation. Stat Comput 23, 271–285 (2013). https://doi.org/10.1007/s11222-011-9308-2
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DOI: https://doi.org/10.1007/s11222-011-9308-2