Approximate Bayesian computation by subset simulation
Chiachio, Manuel and Beck, James L. and Chiachio, Juan and Rus, Guillermo (2014) Approximate Bayesian computation by subset simulation. SIAM Journal on Scientific Computing, 36 (3). 1339–1358. ISSN 1064-8275 (https://doi.org/10.1137/130932831)
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Abstract
A new approximate Bayesian computation (ABC) algorithm for Bayesian updating of model parameters is proposed in this paper, which combines the ABC principles with the technique of subset simulation for efficient rare-event simulation, first developed in S. K. Au and J. L. Beck [Probabilistic Engrg. Mech., 16 (2001), pp. 263--277]. It has been named ABC-SubSim. The idea is to choose the nested decreasing sequence of regions in subset simulation as the regions that correspond to increasingly closer approximations of the actual data vector in observation space. The efficiency of the algorithm is demonstrated in two examples that illustrate some of the challenges faced in real-world applications of ABC. We show that the proposed algorithm outperforms other recent sequential ABC algorithms in terms of computational efficiency while achieving the same, or better, measure of accuracy in the posterior distribution. We also show that ABC-SubSim readily provides an estimate of the evidence (marginal likelihood) for posterior model class assessment, as a by-product.
ORCID iDs
Chiachio, Manuel, Beck, James L., Chiachio, Juan ORCID: https://orcid.org/0000-0003-1243-8694 and Rus, Guillermo;-
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Item type: Article ID code: 65616 Dates: DateEvent26 June 2014Published8 April 2014AcceptedSubjects: Science > Mathematics Department: Faculty of Engineering > Naval Architecture, Ocean & Marine Engineering Depositing user: Pure Administrator Date deposited: 02 Oct 2018 11:56 Last modified: 09 Oct 2024 00:28 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/65616