Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems

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Title

Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems

Content partner

The University of Auckland Library

Collection

ResearchSpace@Auckland

Description

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 ...

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Format

Research paper

Research format

Journal article

Date created

2009

Creator

Toni, Tina / Welch, John / Strelkowa, Natalja / Ipsen, Andreas / Stumpf, Michael PH

URL

http://hdl.handle.net/2292/14406

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