A. Dinner, E. Thiede, B. V. Koten, J. Weare
tlooto Summary
It is shown that EMUS can be dramatically more efficient than direct MCMC when the target distribution is multimodal or when the goal is to compute tail probabilities.
Abstract
The Eigenvector Method for Umbrella Sampling (EMUS) [46] belongs to a popular class of methods in statistical mechanics which adapt the principle of stratified survey sampling to the computation of free energies. We develop a detailed theoretical analysis of EMUS. Based on this analysis, we show that EMUS is an efficient general method for computing averages over arbitrary target distributions. In particular, we show that EMUS can be dramatically more efficient than direct MCMC when the target distribution is multimodal or when the goal is to compute tail probabilities. To illustrate these theoretical results, we present a tutorial application of the method to a problem from Bayesian statistics.
Citation format
DINNER, A., et al. Stratification as a general variance reduction method for markov chain monte carlo. SIAM-ASA Journal on Uncertainty Quantification, 2017, 8: 1139–1188.