Adaptive Importance Sampling for MarkovChains on General State Spaces 1

Adaptive importance sampling involves successively estimating the function of interest and then constructing an importance sampling scheme built on the estimate. Here, we investigate such a scheme used in simulations of Markov chains derived from particle transport problems. Previous work had shown that for nite state spaces the convergence was exponential, which veri ed computational experience. Here, these results are extended to general state spaces.