Aderhold Andrej, Husmeier Dirk, Grzegorczyk Marco
1School of Mathematics and Statistics, Glasgow University, Glasgow, UK.
2Johann Bernoulli Institute (JBI), Groningen University, Groningen, The Netherlands.
Stat Comput. 2017;27(4):1003-1040. doi: 10.1007/s11222-016-9668-8. Epub 2016 Jun 16.
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.
由微分方程系统表示的相互作用网络的推断在许多科学学科中都是一个具有挑战性的问题。在本文中，我们采用基于梯度匹配的半机械建模方法。我们研究了包括动力学模型、统计公式和数值方法在内的关键因素对网络重建性能的影响程度。我们强调了计算统计学家在面对模型选择挑战时的一般经验教训，并评估了各种替代范式的准确性，包括最近广泛适用的信息准则和用于近似贝叶斯因子的不同数值程序。我们使用一种新颖的推理管道进行比较评估，该管道通过方差分析方案系统地消除混杂因素。
