Metropolized Randomized Maximum Likelihood for sampling from multimodal distributions

Abstract

This article describes a method for using optimization to derive efficient independent transition functions for Markov chain Monte Carlo simulations. Our interest is in sampling from a posterior density $\pi (x)$ for problems in which the dimension of the model space is large, $\pi (x)$ is multimodal with regions of low probability separating the modes, and evaluation of the likelihood is expensive. We restrict our attention to the special case for which the target density is the product of a multivariate Gaussian prior and a likelihood function for which the errors in observations are additive and Gaussian.

References in corpus (3)

• The pseudo-marginal approach for efficient Monte Carlo computations
• A General Framework for the Parametrization of Hierarchical Models
• On the efficiency of pseudo-marginal random walk Metropolis algorithms

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• Optimization-Based MCMC Methods for Nonlinear Hierarchical Statistical Inverse Problems