Adaptive approximate Bayesian computation for complex models
- 806 Downloads
We propose a new approximate Bayesian computation (ABC) algorithm that aims at minimizing the number of model runs for reaching a given quality of the posterior approximation. This algorithm automatically determines its sequence of tolerance levels and makes use of an easily interpretable stopping criterion. Moreover, it avoids the problem of particle duplication found when using a MCMC kernel. When applied to a toy example and to a complex social model, our algorithm is 2–8 times faster than the three main sequential ABC algorithms currently available.
KeywordsABC Population Monte Carlo Sequential Monte Carlo
This publication has been funded by the Prototypical policy impacts on multifunctional activities in rural municipalities collaborative project, European Union 7th Framework Programme (ENV 2007-1), contract no. 212345. The work of the first author has been funded by the Auvergne region.
- Beaumont MA (2010) Approximate Bayesian computation in evolution and ecology. Annu Rev Ecol Evol Syst 41(1):379–406Google Scholar
- Beaumont MA, Cornuet J, Marin J, Robert CP (2009) Adaptive approximate Bayesian computation. Biometrika 96(4):983–990Google Scholar
- Beaumont MA, Zhang W, Balding DJ (2002) Approximate Bayesian computation in population genetics. Genetics 162(4):2025–2035Google Scholar
- Carnell R (2009) lhs: Latin hypercube samples. R package version 0.5Google Scholar
- Fearnhead P, Prangle D (2011) Constructing summary statistics for approximate Bayesian computation: semi-automatic ABC. Technical report 1004.1112. arXiv.orgGoogle Scholar
- Filippi S, Barnes C, Stumpf MPH (2012) On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo. arXiv:1106.6280v4Google Scholar
- Huet S, Deffuant G (2011) Common framework for the microsimulation model in prima project. Technical report, Cemagref LISCGoogle Scholar
- Jabot F, Faure T, Dumoulin N (2013) EasyABC: performing efficient approximate Bayesian computation sampling schemes using R. Methods Ecol Evol (in press). doi: 10.1111/2041-210X.12050
- Joyce P, Marjoram P (2008) Approximately sufficient statistics and Bayesian computation. Stat Appl Genet Mol Biol 7(1):1–18Google Scholar
- R Development Core Team (2011) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0Google Scholar