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 |
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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: | Husmeier, Professor Dirk and Aderhold, Mr Andrej |
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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