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A scheme for adaptive selection of population sizes in approximate Bayesian Computation - Sequential Monte Carlo.
Lecture Notes Comp. Sci. 10545 LNBI, 128-144 (2017)
Parameter inference and model selection in systems biology often requires likelihood-free methods, such as Approximate Bayesian Computation (ABC). In recent years, this approach has frequently been combined with a Sequential Monte Carlo (ABC-SMC) scheme. In this scheme, the approximation of the posterior distribution through a population of particles is iteratively improved by a sequential sampling strategy. However, it has been difficult to give general guidelines on how to choose the size of these populations. In this manuscript, we propose a method to adaptively and automatically select these population sizes. The method exploits the cross-validated approximation error of a kernel density estimate of the particles in the current population to select the number of particles for the subsequent population. We found the proposed method to be robust to the initially chosen population size and to the number of posterior modes. We demonstrated that the method is applicable to parameter inference as well as to model selection. The study of a computationally demanding multiscale model confirmed the method’s scalability. In conclusion, the proposed method is applicable to a wide range of parameter and model selection tasks. The method makes the influence of the population size on the approximation error explicit simplifying the application of ABC-SMC schemes.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Approximate Bayesian Computation ; Likelihood-free Inference ; Model Selection ; Parameter Estimation ; Population Size ; Sequential Monte Carlo
ISSN (print) / ISBN 0302-9743
Conference Title 15th International Conference on Computational Methods in Systems Biology, CMSB 2017
Conference Date 27-29 September 2017
Conference Location Darmstadt
Quellenangaben Volume: 10545 LNBI, Pages: 128-144
Publishing Place Berlin [u.a.]
Institute(s) Institute of Computational Biology (ICB)