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Small‐Noise Analysis and Symmetrization of Implicit Monte Carlo Samplers
Author(s) -
Goodman Jonathan,
Lin Kevin K.,
Morzfeld Matthias
Publication year - 2016
Publication title -
communications on pure and applied mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.12
H-Index - 115
eISSN - 1097-0312
pISSN - 0010-3640
DOI - 10.1002/cpa.21592
Subject(s) - symmetrization , mathematics , laplace transform , monte carlo method , noise (video) , bayesian probability , sampling (signal processing) , rejection sampling , algorithm , markov chain monte carlo , statistics , computer science , hybrid monte carlo , mathematical analysis , artificial intelligence , filter (signal processing) , image (mathematics) , computer vision
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.