Small-Noise Analysis and Symmetrization of Implicit Monte Carlo Samplers

Jonathan Goodman, Kevin K. Lin, Matthias Morzfeld

Research output: Contribution to journalArticle


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.

Original languageEnglish (US)
JournalCommunications on Pure and Applied Mathematics
StateAccepted/In press - 2015


ASJC Scopus subject areas

  • Mathematics(all)
  • Applied Mathematics

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