Sequential Monte Carlo with Adaptive Weights for Approximate Bayesian Computation

Fernando V. Bonassi; Mike West

doi:10.1214/14-ba891

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arXiv · 1503.07791

Sequential Monte Carlo with Adaptive Weights for Approximate Bayesian Computation

Fernando V. Bonassi·Mike West

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Abstract

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.

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Fernando V. Bonassi, Mike West. 2015-03-26. Sequential Monte Carlo with Adaptive Weights for Approximate Bayesian Computation. https://doi.org/10.1214/14-ba891

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