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- W3124497799 abstract "Approximate Bayesian computation (ABC) is a family of computational techniques in Bayesian statistics. These techniques allow to fi t a model to data without relying on the computation of the model likelihood. They instead require to simulate a large number of times the model to be fi tted. A number of re finements to the original rejection-based ABC scheme have been proposed, including the sequential improvement of posterior distributions. This technique allows to de- crease the number of model simulations required, but it still presents several shortcomings which are particu- larly problematic for costly to simulate complex models. We here provide a new algorithm to perform adaptive approximate Bayesian computation, which is shown to perform better on both a toy example and a complex social model." @default.
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- W3124497799 date "2013-07-03" @default.
- W3124497799 modified "2023-10-11" @default.
- W3124497799 title "Adaptive approximate Bayesian computation for complex models" @default.
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- W3124497799 doi "https://doi.org/10.1007/s00180-013-0428-3" @default.
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