Multilevel Monte Carlo in approximate Bayesian computation

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

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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
Issue number3
StatePublished - Jan 31 2019

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