Multilevel Monte Carlo in approximate Bayesian computation

Ajay Jasra*, Seongil Jo, David Nott, Christine Shoemaker, Raul Tempone

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

In the following article, we consider approximate Bayesian computation (ABC) inference. We introduce a method for numerically approximating ABC posteriors using the multilevel Monte Carlo (MLMC). A sequential Monte Carlo version of the approach is developed and it is shown under some assumptions that for a given level of mean square error, this method for ABC has a lower cost than i.i.d. sampling from the most accurate ABC approximation. Several numerical examples are given.

Original languageEnglish (US)
Pages (from-to)346-360
Number of pages15
JournalStochastic Analysis and Applications
Volume37
Issue number3
DOIs
StatePublished - May 4 2019

Bibliographical note

Publisher Copyright:
© 2019, © 2019 Taylor & Francis Group, LLC.

Keywords

  • Approximate Bayesian computation
  • multilevel Monte Carlo
  • sequential Monte Carlo

ASJC Scopus subject areas

  • Applied Mathematics
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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