Quasi-Monte Carlo and Multilevel Monte Carlo Methods for Computing Posterior Expectations in Elliptic Inverse Problems

R. Scheichl, A. M. Stuart, A. L. Teckentrup

Research output: Contribution to journalArticlepeer-review

Abstract

We are interested in computing the expectation of a functional of a PDE solution under a Bayesian posterior distribution. Using Bayes' rule, we reduce the problem to estimating the ratio of two related prior expectations. For a model elliptic problem, we provide a full convergence and complexity analysis of the ratio estimator in the case where Monte Carlo, quasi-Monte Carlo or multilevel Monte Carlo methods are used as estimators for the two prior expectations. We show that the computational complexity of the ratio estimator to achieve a given accuracy is the same as the corresponding complexity of the individual estimators for the numerator and the denominator. We {also include numerical simulations, in the context of the model elliptic problem, which demonstrate the effectiveness of the approach.
Original languageEnglish
Pages (from-to)493-518
Number of pages26
JournalSIAM/ASA Journal on Uncertainty Quantification
Volume5
Issue number1
DOIs
Publication statusPublished - 27 Apr 2017

Keywords

  • math.NA

Fingerprint

Dive into the research topics of 'Quasi-Monte Carlo and Multilevel Monte Carlo Methods for Computing Posterior Expectations in Elliptic Inverse Problems'. Together they form a unique fingerprint.

Cite this