Approximate Bayesian computation

Mark A. Beaumont

doi:10.1146/annurev-statistics-030718-105212

Approximate Bayesian computation

Mark A. Beaumont^*

^*Corresponding author for this work

Research output: Contribution to journal › Review article (Academic Journal) › peer-review

158 Citations (Scopus)

Abstract

Many of the statistical models that could provide an accurate, interesting, and testable explanation for the structure of a data set turn out to have intractable likelihood functions. The method of approximate Bayesian computation (ABC) has become a popular approach for tackling such models. This review gives an overview of the method and the main issues and challenges that are the subject of current research.

Original language	English
Pages (from-to)	379-403
Number of pages	25
Journal	Annual Review of Statistics and Its Application
Volume	6
Early online date	28 Nov 2018
DOIs	https://doi.org/10.1146/annurev-statistics-030718-105212
Publication status	Published - 7 Mar 2019

Keywords

Bayesian
intractable likelihood
Monte Carlo

Access to Document

10.1146/annurev-statistics-030718-105212

Handle.net

Persistent link

Cite this

@article{4fbe7f91132841889bfbf29a77a1baed,

title = "Approximate Bayesian computation",

abstract = "Many of the statistical models that could provide an accurate, interesting, and testable explanation for the structure of a data set turn out to have intractable likelihood functions. The method of approximate Bayesian computation (ABC) has become a popular approach for tackling such models. This review gives an overview of the method and the main issues and challenges that are the subject of current research.",

keywords = "Bayesian, intractable likelihood, Monte Carlo",

author = "Beaumont, \{Mark A.\}",

year = "2019",

month = mar,

day = "7",

doi = "10.1146/annurev-statistics-030718-105212",

language = "English",

volume = "6",

pages = "379--403",

journal = "Annual Review of Statistics and Its Application",

issn = "2326-8298",

publisher = "Annual Reviews Inc.",

}

TY - JOUR

T1 - Approximate Bayesian computation

AU - Beaumont, Mark A.

PY - 2019/3/7

Y1 - 2019/3/7

N2 - Many of the statistical models that could provide an accurate, interesting, and testable explanation for the structure of a data set turn out to have intractable likelihood functions. The method of approximate Bayesian computation (ABC) has become a popular approach for tackling such models. This review gives an overview of the method and the main issues and challenges that are the subject of current research.

AB - Many of the statistical models that could provide an accurate, interesting, and testable explanation for the structure of a data set turn out to have intractable likelihood functions. The method of approximate Bayesian computation (ABC) has become a popular approach for tackling such models. This review gives an overview of the method and the main issues and challenges that are the subject of current research.

KW - Bayesian

KW - intractable likelihood

KW - Monte Carlo

UR - https://www.scopus.com/pages/publications/85062838468

U2 - 10.1146/annurev-statistics-030718-105212

DO - 10.1146/annurev-statistics-030718-105212

M3 - Review article (Academic Journal)

AN - SCOPUS:85062838468

SN - 2326-8298

VL - 6

SP - 379

EP - 403

JO - Annual Review of Statistics and Its Application

JF - Annual Review of Statistics and Its Application

ER -

Approximate Bayesian computation

Abstract

Keywords

Access to Document

Handle.net

Other files and links

Fingerprint

Cite this