Adaptive approximate Bayesian computation for complex models

Maxime Lenormand (), Franck Jabot () and Guillaume Deffuant ()

Computational Statistics, 2013, vol. 28, issue 6, 2777-2796

Abstract: We propose a new approximate Bayesian computation (ABC) algorithm that aims at minimizing the number of model runs for reaching a given quality of the posterior approximation. This algorithm automatically determines its sequence of tolerance levels and makes use of an easily interpretable stopping criterion. Moreover, it avoids the problem of particle duplication found when using a MCMC kernel. When applied to a toy example and to a complex social model, our algorithm is 2–8 times faster than the three main sequential ABC algorithms currently available. Copyright Springer-Verlag Berlin Heidelberg 2013

Keywords: ABC; Population Monte Carlo; Sequential Monte Carlo (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (11)

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DOI: 10.1007/s00180-013-0428-3

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