Approximate Bayesian inference in semi-mechanistic models

Aderhold, A. and Husmeier, D. and Grzegorczyk, M. (2016) Approximate Bayesian inference in semi-mechanistic models. [Data Collection]

Enlighten Publications URI: http://eprints.gla.ac.uk/id/eprint/120872

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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.

College / School: College of Science and Engineering > School of Mathematics and Statistics
Date Deposited: 23 Nov 2016 15:27
Related resource URL: http://researchdata.gla.ac.uk/351/
Retention date: 23 November 2026
Funder's Name: Engineering & Physical Sciences Research Council (EPSRC), European Commission (EC)
URI: http://researchdata.gla.ac.uk/id/eprint/374

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Aderhold, A. and Husmeier, D. and Grzegorczyk, M. (2016); Approximate Bayesian inference in semi-mechanistic models

University of Glasgow

10.5525/gla.researchdata.374

Retrieved: 2017-09-22

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