Importance Sampling: Intrinsic Dimension and Computational Cost

Abstract

The basic idea of importance sampling is to use independent samples from a proposal measure in order to approximate expectations with respect to a target measure. It is key to understand how many samples are required in order to guarantee accurate approximations. Intuitively, some notion of distance between the target and the proposal should determine the computational cost of the method. A major challenge is to quantify this distance in terms of parameters or statistics that are pertinent for the practitioner. The subject has attracted substantial interest from within a variety of communities. The objective of this paper is to overview and unify the resulting literature by creating an overarching framework. A general theory is presented, with a focus on the use of importance sampling in Bayesian inverse problems and filtering.

Statistical Science

References in corpus (2)

• A Stable Particle Filter in High-Dimensions
• Preconditioning the prior to overcome saturation in Bayesian inverse problems

Cited by in corpus (6)

• Effective Sample Size for Importance Sampling based on discrepancy measures
• Hessian-based adaptive sparse quadrature for infinite-dimensional Bayesian inverse problems
• On well-posedness of Bayesian data assimilation and inverse problems in Hilbert space
• Continuum Limit of Posteriors in Graph Bayesian Inverse Problems
• Quasi-Monte Carlo and Multilevel Monte Carlo Methods for Computing Posterior Expectations in Elliptic Inverse Problems
• Sequential Ensemble Transform for Bayesian Inverse Problems