Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems.
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.
Toni
Tina
T
Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, UK. ttoni@imperial.ac.uk
Welch
David
D
Strelkowa
Natalja
N
Ipsen
Andreas
A
Stumpf
Michael P H
MP
D000465:Algorithms; D001499:Bayes Theorem; D003139:Common Cold / Q000453:epidemiology; D003141:Communicable Diseases / Q000453:epidemiology; D003198:Computer Simulation; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method