Approximate Bayesian inference in semi-mechanistic models

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http://eprints.org/relation/isVersionOf https://researchdata.gla.ac.uk/id/document/1044 http://eprints.org/relation/isVolatileVersionOf https://researchdata.gla.ac.uk/id/document/1044 http://eprints.org/relation/isIndexCodesVersionOf https://researchdata.gla.ac.uk/id/document/1044 archive 5 disk0/00/00/03/74 2016-11-23 15:27:32 2025-05-22 09:49:07 2016-11-23 15:29:04 data_collection show Aderhold Andrej 31743 Husmeier Dirk 17166 0000-0003-1673-7413 Grzegorczyk M. glaresearchdata:2016-11-23-374 Approximate Bayesian inference in semi-mechanistic models pub 30500000 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. 2016-11-23 published University of Glasgow 10.5525/gla.researchdata.374 Software 63329 1 Computational inference in systems biology Dirk Husmeier Engineering & Physical Sciences Research Council (EPSRC) EP/L020319/1 M&S - STATISTICS 59237 1 TIMET: Linking the clock to metabolism Dirk Husmeier European Commission (EC) FP7 245143 TIME M&S - STATISTICS FALSE en http://dx.doi.org/10.5525/gla.researchdata.351 data http://dx.doi.org/10.1007/s11222-016-9668-8 article 2026-11-23 R FALSE FALSE Approximate Bayesian inference in semi-mechanistic models http://eprints.gla.ac.uk/id/eprint/120872