Adaptive approximate Bayesian computation

Abstract

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.

8 pages, 2 figures, one algorithm, third revised resubmission to Biometrika

References in corpus (3)

• Inferring population history with DIYABC: a user-friendly approach to Approximate Bayesian Computation
• Adaptive Importance Sampling in General Mixture Classes
• Convergence of adaptive mixtures of importance sampling schemes

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