Sequential Monte Carlo with Adaptive Weights for Approximate Bayesian Computation

• Activity Number: 297
• Type: Topic Contributed
• Date/Time: Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Fernando Bonassi*+ and Mike West
• Companies: Duke University and Duke University
• Keywords: Adaptive simulation ; approximate Bayesian computation (ABC) ; dynamic bionetwork models ; importance sampling ; mixture model emulators ; sequential Monte Carlo (SMC)

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.