Approximate Bayesian computation for a class of time series models

Ajay Jasra

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Summary: In the following article, we consider approximate Bayesian computation (ABC) for certain classes of time series models. In particular, we focus upon scenarios where the likelihoods of the observations and parameter are intractable, by which we mean that one cannot evaluate the likelihood even up to a non-negative unbiased estimate. This paper reviews and develops a class of approximation procedures based upon the idea of ABC, but specifically maintains the probabilistic structure of the original statistical model. This latter idea is useful, in that one can adopt or adapt established computational methods for statistical inference. Several existing results in the literature are surveyed, and novel developments with regards to computation are given.
Original languageEnglish (US)
JournalInternational Statistical Review
Issue number3
StatePublished - Dec 1 2015
Externally publishedYes


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