Toni Tina, Welch David, Strelkowa Natalja, Ipsen Andreas, Stumpf Michael P H
Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, UK.
J R Soc Interface. 2009 Feb 6;6(31):187-202. doi: 10.1098/rsif.2008.0172.
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.
近似贝叶斯计算（ABC）方法可用于评估后验分布，而无需计算似然性。在本文中，我们讨论并应用一种基于序贯蒙特卡罗（SMC）的ABC方法来估计动力学模型的参数。我们表明，ABC SMC提供了有关参数可推断性以及模型对参数变化的敏感性的信息，并且往往比其他ABC方法表现更好。该算法应用于几个著名的生物系统，推断出了这些系统的参数及其可信区间。此外，我们将ABC SMC开发为一种模型选择工具；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工具选择最佳模型。
