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Adaptive approximate Bayesian computation

Beaumont, M. A., Cornuet, J. M., Marin, J. M. and Robert, C. P. (2009) Adaptive approximate Bayesian computation. Biometrika, 96 (4). pp. 983-990. ISSN 0006-3444

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To link to this item DOI: 10.1093/biomet/asp052

Abstract/Summary

Sequential techniques can enhance the efficiency of the approximate Bayesian computation algorithm, as in Sisson et al.'s (2007) partial rejection control version. While this method is based upon the theoretical works of Del Moral et al. (2006), the application to approximate Bayesian computation results in a bias in the approximation to the posterior. An alternative version based on genuine importance sampling arguments bypasses this difficulty, in connection with the population Monte Carlo method of Cappe et al. (2004), and it includes an automatic scaling of the forward kernel. When applied to a population genetics example, it compares favourably with two other versions of the approximate algorithm.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Biological Sciences
ID Code:9552
Uncontrolled Keywords:Importance sampling, Markov chain Monte Carlo, Partial rejection, control, Sequential Monte Carlo, SEQUENTIAL MONTE-CARLO, POPULATION, LIKELIHOODS

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