We are interested in computing the expectation of a functional of a PDE solution under a Bayesian posterior distribution. Using Bayes's 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.
FingerprintDive 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.
- Department of Mathematical Sciences - Professor, Visiting Professor
- EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa)
- Water Innovation and Research Centre (WIRC)
- Probability Laboratory at Bath
Person: Research & Teaching, Honorary / Visiting Staff