Approximate Bayesian inference in semi-mechanistic models

Aderhold, A., Husmeier, D. and Grzegorczyk, M. (2017) Approximate Bayesian inference in semi-mechanistic models. Statistics and Computing, 27(4), pp. 1003-1040. (doi:10.1007/s11222-016-9668-8)

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Abstract

Inference of interaction networks represented by systems of differential equations is a challenging problem in many scientific disciplines. In the present article, we follow a semi-mechanistic modelling approach based on gradient matching. We investigate the extent to which key factors, including the kinetic model, statistical formulation and numerical methods, impact upon performance at network reconstruction. We emphasize general lessons for computational statisticians when faced with the challenge of model selection, and we assess the accuracy of various alternative paradigms, including recent widely applicable information criteria and different numerical procedures for approximating Bayes factors. We conduct the comparative evaluation with a novel inferential pipeline that systematically disambiguates confounding factors via an ANOVA scheme.

Item Type:Articles
Keywords:Network inference, semi-mechanistic model, Bayesian model selection, widely applicable information criteria (WAIC, WBIC ), Markov jump processes, ANOVA, systems biology.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Aderhold, Mr Andrej and Husmeier, Professor Dirk
Authors: Aderhold, A., Husmeier, D., and Grzegorczyk, M.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Statistics and Computing
Publisher:Springer
ISSN:0960-3174
ISSN (Online):1573-1375
Published Online:16 June 2016
Copyright Holders:Copyright © 2016 The Authors
First Published:First published in Statistics and Computing 2016
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
633291Computational inference in systems biologyDirk HusmeierEngineering & Physical Sciences Research Council (EPSRC)EP/L020319/1M&S - STATISTICS
592371TIMET: Linking the clock to metabolismDirk HusmeierEuropean Commission (EC)FP7 245143 TIMEM&S - STATISTICS