Sequential monte carlo with adaptive weights for approximate bayesian computation


Methods of approximate Bayesian computation (ABC) are increasingly used for analysis of complex models. A major challenge for ABC is overcoming 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.

Full Text

Duke Authors

Cited Authors

  • Bonassi, FV; West, M

Published Date

  • January 1, 2015

Published In

Start / End Page

  • 171 - 187

Electronic International Standard Serial Number (EISSN)

  • 1931-6690

International Standard Serial Number (ISSN)

  • 1936-0975

Digital Object Identifier (DOI)

  • 10.1214/14-BA891

Citation Source

  • Scopus