Bayesian Analysis

Sequential Monte Carlo with Adaptive Weights for Approximate Bayesian Computation

Fernando V. Bonassi and Mike West

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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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Bayesian Anal., Volume 10, Number 1 (2015), 171-187.

First available in Project Euclid: 28 January 2015

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complex modelling adaptive simulation dynamic bionetwork models importance sampling mixture model emulators


Bonassi, Fernando V.; West, Mike. Sequential Monte Carlo with Adaptive Weights for Approximate Bayesian Computation. Bayesian Anal. 10 (2015), no. 1, 171--187. doi:10.1214/14-BA891.

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