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Adaptive approximate Bayesian computation

Robert, Christian P.; Marin, Jean-Michel; Cornuet, Jean-Marie; Beaumont, Mark A. (2009), Adaptive approximate Bayesian computation, Biometrika, 96, 4, p. 983-990. http://dx.doi.org/10.1093/biomet/asp052

Type
Article accepté pour publication ou publié
External document link
http://hal.archives-ouvertes.fr/hal-00280461/en/
Date
2009
Journal name
Biometrika
Volume
96
Number
4
Publisher
Oxford University Press
Pages
983-990
Publication identifier
http://dx.doi.org/10.1093/biomet/asp052
Metadata
Show full item record
Author(s)
Robert, Christian P.
Marin, Jean-Michel cc
Cornuet, Jean-Marie
Beaumont, Mark A.
Abstract (EN)
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 Cappé 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.
Subjects / Keywords
Importance sampling; Markov chain Monte Carlo; Partial rejection control; Sequential Monte Carlo

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