Statistical Science

Importance Sampling: Intrinsic Dimension and Computational Cost

S. Agapiou, O. Papaspiliopoulos, D. Sanz-Alonso, and A. M. Stuart

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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.

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Statist. Sci. Volume 32, Number 3 (2017), 405-431.

First available in Project Euclid: 1 September 2017

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Importance sampling notions of dimension small noise absolute continuity inverse problems filtering


Agapiou, S.; Papaspiliopoulos, O.; Sanz-Alonso, D.; Stuart, A. M. Importance Sampling: Intrinsic Dimension and Computational Cost. Statist. Sci. 32 (2017), no. 3, 405--431. doi:10.1214/17-STS611.

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Supplemental materials

  • Supplement to “Importance sampling: Intrinsic dimension and computational cost”. The Supplementary Material contains the proofs of all our results. It also contains some background on Gaussian measures on Hilbert spaces, and related technical aspects arising from considering measures in infinite dimensional spaces.