The asymptotic efficiency of simulation estimators
Operations Research
Non-linear regression models for Approximate Bayesian Computation
Statistics and Computing
An adaptive sequential Monte Carlo method for approximate Bayesian computation
Statistics and Computing
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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.