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Approximate Bayesian inference in semi-mechanistic models. (English) Zbl 1384.62075
Summary: 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.

MSC:
62F15 Bayesian inference
62J10 Analysis of variance and covariance (ANOVA)
62P10 Applications of statistics to biology and medical sciences; meta analysis
92C42 Systems biology, networks
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