期刊
COMPUTATIONAL STATISTICS
卷 28, 期 6, 页码 2777-2796出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s00180-013-0428-3
关键词
ABC; Population Monte Carlo; Sequential Monte Carlo
资金
- European Union [ENV 2007-1, 212345]
- Auvergne region
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.
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