Adaptive approximate Bayesian computation
University of Bristol · University of Reading · +8 more institutions
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 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.
Citation impact
- FWCI
- 26.97
- Percentile
- 100%
- References
- 16
Authors
4- MBMark BeaumontCorresponding
University of Bristol, University of Reading
- JMJ. M. Cornuet
Centre de Biologie et de Gestion des Populations, Imperial College London, Agropolis International
- JMJean‐Michel Marin
Institut Montpelliérain Alexander Grothendieck, Université de Montpellier
- CPChristian P. Robert
Centre de Recherche en Économie et Statistique, Université Paris Dauphine-PSL, Centre de Recherche en Mathématiques de la Décision
Topics & keywords
- Approximate Bayesian computation
- Mathematics
- Computation
- Bayesian probability
- Kernel (algebra)
- Algorithm
- Population
- Monte Carlo method