Multilevel Sequential Monte Carlo Samplers for Normalizing Constants

Abstract

This article considers the sequential Monte Carlo (SMC) approximation of ratios of normalizing constants associated to posterior distributions which in principle rely on continuum models. Therefore, the Monte Carlo estimation error and the discrete approximation error must be balanced. A multilevel strategy is utilized to substantially reduce the cost to obtain a given error level in the approximation as compared to standard estimators. Two estimators are considered and relative variance bounds are given. The theoretical results are numerically illustrated for the example of identifying a parametrized permeability in an elliptic equation given point-wise observations of the pressure.

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• Multilevel Sequential Monte Carlo with Dimension-Independent Likelihood-Informed Proposals
• Bayesian Static Parameter Estimation for Partially Observed Diffusions via Multilevel Monte Carlo