Adaptive approximate Bayesian computation
AbstractSequential 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. Copyright 2009, Oxford University Press.
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Bibliographic InfoArticle provided by Biometrika Trust in its journal Biometrika.
Volume (Year): 96 (2009)
Issue (Month): 4 ()
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- Michael Creel & Dennis Kristensen, 2011.
"Indirect likelihood inference,"
UFAE and IAE Working Papers
874.11, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
- Drovandi, Christopher C. & Pettitt, Anthony N., 2011. "Likelihood-free Bayesian estimation of multivariate quantile distributions," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2541-2556, September.
- Erhardt, Robert J. & Smith, Richard L., 2012. "Approximate Bayesian computing for spatial extremes," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1468-1481.
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