Markov chain importance sampling with applications to rare event probability estimation
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.
importance sampling rare-event probability nonparametric minimum variance density variance reduction