Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems
TLDR
This paper discusses and applies an ABC method based on sequential Monte Carlo (SMC) to estimate parameters of dynamical models and develops ABC SMC as a tool for model selection; given a range of different mathematical descriptions, it is able to choose the best model using the standard Bayesian model selection apparatus.Abstract:
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.read more
Citations
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Journal ArticleDOI
Stochastic Processes in Physics and Chemistry
TL;DR: Van Kampen as mentioned in this paper provides an extensive graduate-level introduction which is clear, cautious, interesting and readable, and could be expected to become an essential part of the library of every physical scientist concerned with problems involving fluctuations and stochastic processes.
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Particle Markov chain Monte Carlo methods
TL;DR: It is shown here how it is possible to build efficient high dimensional proposal distributions by using sequential Monte Carlo methods, which allows not only to improve over standard Markov chain Monte Carlo schemes but also to make Bayesian inference feasible for a large class of statistical models where this was not previously so.
Journal ArticleDOI
Approximate Bayesian Computation (ABC) in practice
TL;DR: It is argued that the use of ABC should incorporate all aspects of Bayesian data analysis: formulation, fitting, and improvement of a model if these principles are carefully applied.
Journal ArticleDOI
Modelling the influence of human behaviour on the spread of infectious diseases: a review
TL;DR: Recent efforts to incorporate human behaviour into disease models are reviewed, and it is proposed that such models can be broadly classified according to the type and source of information which individuals are assumed to base their behaviour on, andAccording to the assumed effects of such behaviour.
Journal ArticleDOI
Approximate Bayesian Computation in Evolution and Ecology
TL;DR: Although the method arose in population genetics, ABC is increasingly used in other fields, including epidemiology, systems biology, ecology, and agent-based modeling, and many of these applications are briefly described.
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