Multilevel Sequential Monte Carlo Samplers for Normalizing Constants

Pierre Del Moral 1 Ajay Jasra 2 Kody J. H. Law 3 Yan Zhou 4
1 CQFD - Quality control and dynamic reliability
IMB - Institut de Mathématiques de Bordeaux, Inria Bordeaux - Sud-Ouest
2 NUS - National University of Singapore
3 ORNL - Oak Ridge National Laboratory [Oak Ridge]
4 BGI - Beijing Genomics Institute [Shenzhen]
Abstract : This article considers the Sequential Monte Carlo (SMC) approximation of ratios of normalizing constants associated to posterior distributions which in principle rely on continuum models. Therefore, the Monte Carlo estimation error and the discrete approximation error must be balanced. A multilevel strategy is utilized to substantially reduce the cost to obtain a given error level in the approximation as compared to standard estimators. Two estimators are considered and relative variance bounds are given. The theoretical results are numerically illustrated for two Bayesian inverse problems arising from elliptic Partial Differential Equations (PDEs). The examples involve the inversion of observations of the solution of (i) a one-dimensional Poisson equation to infer the diffusion coefficient, and (ii) a two-dimensional Poisson equation to infer the external forcing.
Document type :
Journal articles
Liste complète des métadonnées
https://hal.inria.fr/hal-01669115
Contributor : Pierre Del Moral <>
Submitted on : Wednesday, December 20, 2017 - 3:36:01 PM
Last modification on : Tuesday, February 19, 2019 - 2:20:02 PM

Identifiers

Collections

INRIA-BORDEAUX | INSMI | IMB | INRIA | INRIA2017

Citation

Pierre Del Moral, Ajay Jasra, Kody J. H. Law, Yan Zhou. Multilevel Sequential Monte Carlo Samplers for Normalizing Constants. ACM Transactions on Modeling and Computer Simulation, Association for Computing Machinery, 2017, 27 (3), pp.1 - 22. ⟨10.1145/3092841⟩. ⟨hal-01669115⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

189

Contact


archive-ouverte@inria.fr