Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems

被引：981

作者：

Toni, Tina ^{[1
,2
]}

Welch, David ^{[3
]}

Strelkowa, Natalja ^{[4
]}

Ipsen, Andreas ^{[5
]}

Stumpf, Michael P. H. ^{[1
,2
]}

机构：

[1] Univ London Imperial Coll Sci Technol & Med, Ctr Bioinformat, Div Mol Biosci, London SW7 2AZ, England

[2] Univ London Imperial Coll Sci Technol & Med, Inst Math Sci, London SW7 2AZ, England

[3] Univ London Imperial Coll Sci Technol & Med, Dept Epidemiol & Publ Hlth, London SW7 2AZ, England

[4] Univ London Imperial Coll Sci Technol & Med, Dept Bioengn, London SW7 2AZ, England

[5] Univ London Imperial Coll Sci Technol & Med, Dept Biomol Med, London SW7 2AZ, England

来源：

JOURNAL OF THE ROYAL SOCIETY INTERFACE | 2009年 / 6卷 / 31期

基金：

英国生物技术与生命科学研究理事会; 英国工程与自然科学研究理事会; 英国惠康基金;

关键词：

sequential Monte Carlo; Bayesian model selection; sequential importance sampling; parameter estimation; dynamical systems; sloppy parameters; SEQUENTIAL MONTE-CARLO; BIOCHEMICAL PATHWAYS; OPTIMIZATION; NETWORK;

D O I：

10.1098/rsif.2008.0172

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Approximate Bayesian computation (ABC) methods can be used to evaluate posterior distributions without having to calculate likelihoods. In this paper, we discuss and apply an ABC method based on sequential Monte Carlo (SMC) to estimate parameters of dynamical models. We show that ABC SMC provides information about the inferability of parameters and model sensitivity to changes in parameters, and tends to perform better than other ABC approaches. The algorithm is applied to several well-known biological systems, for which parameters and their credible intervals are inferred. Moreover, we develop ABC SMC as a tool for model selection; given a range of different mathematical descriptions, ABC SMC is able to choose the best model using the standard Bayesian model selection apparatus.

引用

页码：187 / 202

页数：16