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

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          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.

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          Monte Carlo Statistical Methods

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            Sequential Monte Carlo samplers

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              Theoretical Statistics

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                Author and article information

                Journal
                Journal of The Royal Society Interface
                J. R. Soc. Interface.
                The Royal Society
                1742-5689
                1742-5662
                February 06 2009
                July 09 2008
                February 06 2009
                : 6
                : 31
                : 187-202
                Affiliations
                [1 ]Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College LondonLondon SW7 2AZ, UK
                [2 ]Institute of Mathematical Sciences, Imperial College LondonLondon SW7 2AZ, UK
                [3 ]Department of Epidemiology and Public Health, Imperial College LondonLondon SW7 2AZ, UK
                [4 ]Department of Bioengineering, Imperial College LondonLondon SW7 2AZ, UK
                [5 ]Department of Biomolecular Medicine, Imperial College LondonLondon SW7 2AZ, UK
                Article
                10.1098/rsif.2008.0172
                2658655
                19205079
                be34918e-d2f7-4b99-92cb-58493d296ff2
                © 2009

                https://royalsociety.org/journals/ethics-policies/data-sharing-mining/


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