Sequential Monte Carlo with Adaptive Weights for Approximate Bayesian Computation
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.
Cite
@article{arxiv.1503.07791,
title = {Sequential Monte Carlo with Adaptive Weights for Approximate Bayesian Computation},
author = {Fernando V. Bonassi and Mike West},
journal= {arXiv preprint arXiv:1503.07791},
year = {2015}
}
Comments
Published at http://dx.doi.org/10.1214/14-BA891 in the Bayesian Analysis (http://projecteuclid.org/euclid.ba) by the International Society of Bayesian Analysis (http://bayesian.org/)