TY - JOUR

T1 - Approximate Bayesian computation for a class of time series models

AU - Jasra, Ajay

N1 - Generated from Scopus record by KAUST IRTS on 2019-11-20

PY - 2015/12/1

Y1 - 2015/12/1

N2 - 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.

AB - 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.

UR - http://doi.wiley.com/10.1111/insr.12089

UR - http://www.scopus.com/inward/record.url?scp=84947490213&partnerID=8YFLogxK

U2 - 10.1111/insr.12089

DO - 10.1111/insr.12089

M3 - Article

VL - 83

JO - International Statistical Review

JF - International Statistical Review

SN - 1751-5823

IS - 3

ER -