Importance Sampling for Simulation of Markovian Physical Processes

Abstract

Transient Markov chains are sometimes simulated to estimate rare event probabilities. For illustration, chains defined by an airborne particle dispersion model are used to estimate the probabilities that released particles reach various locations. Such estimated probabilities are needed for many purposes, including exposure calculations for affected populations and optimization of detector placement. By using experimental designs for simulation runs and embedding fitted regression models of output data in importance sampling transition kernels, convergence is improved by factors of tens to hundreds.