An approximate likelihood perspective on ABC methods

Karabatsos, George, Leisen, Fabrizio (2018) An approximate likelihood perspective on ABC methods. Statistics Surveys, 12 . pp. 66-104. ISSN 1935-7516. (doi:10.1214/18-SS120)

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http://dx.doi.org/10.1214/18-SS120

Abstract

We are living in the big data era, as current technologies and networks allow for the easy and routine collection of data sets in different disciplines. Bayesian Statistics offers a flexible modeling approach which is attractive for describing the complexity of these datasets. These models often exhibit a likelihood function which is intractable due to the large sample size, high number of parameters, or functional complexity. Approximate Bayesian Computational (ABC) methods provides likelihood-free methods for performing statistical inferences with Bayesian models defined by intractable likelihood functions. The vastity of the literature on ABC methods created a need to review and relate all ABC approaches so that scientists can more readily understand and apply them for their own work. This article provides a unifying review, general representation, and classification of all ABC methods from the view of approximate likelihood theory. This clarifies how ABC methods can be characterized, related, combined, improved, and applied for future research. Possible future research in ABC is then outlined.

Item Type: Article
DOI/Identification number: 10.1214/18-SS120
Subjects: H Social Sciences > HA Statistics
Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science
Depositing User: Fabrizio Leisen
Date Deposited: 09 Jun 2018 06:00 UTC
Last Modified: 29 May 2019 20:36 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/67243 (The current URI for this page, for reference purposes)
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