Journal
JOURNAL OF THE ROYAL SOCIETY INTERFACE
Volume 6, Issue 31, Pages 187-202Publisher
ROYAL SOC
DOI: 10.1098/rsif.2008.0172
Keywords
sequential Monte Carlo; Bayesian model selection; sequential importance sampling; parameter estimation; dynamical systems; sloppy parameters
Categories
Funding
- BBSRC [BB/F005210/1] Funding Source: UKRI
- EPSRC [EP/C533542/1] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BB/F005210/1] Funding Source: researchfish
- Engineering and Physical Sciences Research Council [EP/C533542/1] Funding Source: researchfish
- Biotechnology and Biological Sciences Research Council [BB/F005210/1] Funding Source: Medline
- Wellcome Trust Funding Source: Medline
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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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