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arXiv · 1410.6151

Small-noise analysis and symmetrization of implicit Monte Carlo samplers

Jonathan Goodman·Kevin K. Lin·Matthias Morzfeld
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Abstract

Implicit samplers are algorithms for producing independent, weighted samples from multi-variate 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 algo- rithms that leads to improved (implicit) sampling schemes at a rel- atively small additional cost. Computational experiments confirm the theory and show that symmetrization is effective for small noise sampling problems.

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Jonathan Goodman, Kevin K. Lin, Matthias Morzfeld. 2014-10-22. Small-noise analysis and symmetrization of implicit Monte Carlo samplers. https://arxiv.org/abs/1410.6151

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