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Adaptive approximate Bayesian computation
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Journal Article
Adaptive approximate Bayesian computation
2009
Overview
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.
Publisher
Oxford University Press,Biometrika Trust, University College London,Oxford University Press for Biometrika Trust,Oxford Publishing Limited (England),Oxford University Press (OUP)
