Small‐Noise Analysis and Symmetrization of Implicit Monte Carlo Samplers. Issue 10 (6th July 2015) - Goodman, Jonathan; Lin, Kevin K.; Morzfeld, Matthias
Abstract: Implicit samplers are algorithms for producing independent, weighted samples from multivariate probability distributions. These are often applied in Bayesian data assimilation algorithms. We use Laplace asymptotic expansions to analyze two implicit samplers in the small noise regime. Our analysis suggests a symmetrization of the algorithms that leads to improved implicit sampling schemes at a relatively small additional cost. Computational experiments confirm the theory and show that symmetrization is effective for small noise sampling problems.© 2016 Wiley Periodicals, Inc.