preprint · arXiv (Cornell University) · 2008
Adaptive approximate Bayesian computation
Beaumont, Mark A., Cornuet, Jean-Marie, Marin, Jean-Michel, Robert, Christian P.
Cited by 0Open access
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.
Cite this paper
Beaumont, M. A., Cornuet, J.-M., Marin, J.-M., & Robert, C. P. (2008). Adaptive approximate Bayesian computation. arXiv (Cornell University). https://doi.org/10.48550/arxiv.0805.2256
Beaumont, Mark A., et al. “Adaptive approximate Bayesian computation.” arXiv (Cornell University), 2008, https://doi.org/10.48550/arxiv.0805.2256.
Beaumont, Mark A., Jean-Marie Cornuet, Jean-Michel Marin, and Christian P. Robert. 2008. “Adaptive approximate Bayesian computation.” arXiv (Cornell University). https://doi.org/10.48550/arxiv.0805.2256.
Beaumont, M.A. et al. (2008) ‘Adaptive approximate Bayesian computation’, arXiv (Cornell University). Available at: https://doi.org/10.48550/arxiv.0805.2256.
@misc{beaumont2008adaptive,
title = {{Adaptive approximate Bayesian computation}},
author = {Beaumont, Mark A. and Cornuet, Jean-Marie and Marin, Jean-Michel and Robert, Christian P.},
journal = {arXiv (Cornell University)},
year = {2008},
doi = {10.48550/arxiv.0805.2256}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.