Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems
Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems
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DOI：
10.1098/rsif.2008.0172
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发表时间：
2009-02-06
影响因子：
3.9
通讯作者：
Stumpf, Michael P. H.
中科院分区：
文献类型：
--
作者：
Toni, Tina;Welch, David;Stumpf, Michael P. H.
近似贝叶斯计算（ABC）方法可以用来评估后验分布，而不必计算似然。本文讨论并应用基于序贯蒙特卡罗（SMC）的ABC方法估计动力学模型的参数。我们发现，ABC SMC提供了有关参数的可推断性和模型对参数变化的敏感性的信息，并且往往比其他ABC方法表现得更好。该算法被应用到几个著名的生物系统，参数和它们的可信区间推断。此外，我们开发ABC SMC作为模型选择的工具;给定一系列不同的数学描述，ABC SMC能够使用标准的贝叶斯模型选择装置来选择最佳模型。
Approximate Bayesian computation (ABC) methods can be used to evaluate posterior distributions without having to calculate likelihoods. In this paper, we discuss and apply an ABC method based on sequential Monte Carlo (SMC) to estimate parameters of dynamical models. We show that ABC SMC provides information about the inferability of parameters and model sensitivity to changes in parameters, and tends to perform better than other ABC approaches. The algorithm is applied to several well-known biological systems, for which parameters and their credible intervals are inferred. Moreover, we develop ABC SMC as a tool for model selection; given a range of different mathematical descriptions, ABC SMC is able to choose the best model using the standard Bayesian model selection apparatus.