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Sequential Monte Carlo with Adaptive Weights for Approximate Bayesian Computation

2015; International Society for Bayesian Analysis; Volume: 10; Issue: 1 Linguagem: Inglês

10.1214/14-ba891

1936-0975

Fernando V. Bonassi, Mike West,

Statistical Methods and Bayesian Inference

Methods of approximate Bayesian computation (ABC) are increasingly used for analysis of complex models. A major challenge for ABC is over-coming the often inherent problem of high rejection rates in the accept/reject methods based on prior:predictive sampling. A number of recent developments aim to address this with extensions based on sequential Monte Carlo (SMC) strategies. We build on this here, introducing an ABC SMC method that uses data-based adaptive weights. This easily implemented and computationally trivial extension of ABC SMC can very substantially improve acceptance rates, as is demonstrated in a series of examples with simulated and real data sets, including a currently topical example from dynamic modelling in systems biology applications.