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 publiéExternal document link
http://hal.archives-ouvertes.fr/hal-00280461/en/Date
2009Journal name
BiometrikaVolume
96Number
4Publisher
Oxford University Press
Pages
983-990
Publication identifier
Metadata
Show full item recordAbstract (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 CarloRelated items
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