On sequential Monte Carlo, partial rejection control and approximate Bayesian computation

Abstract

We present a sequential Monte Carlo sampler variant of the partial rejection control algorithm, and show that this variant can be considered as a sequential Monte Carlo sampler with a modified mutation kernel. We prove that the new sampler can reduce the variance of the incremental importance weights when compared with standard sequential Monte Carlo samplers. We provide a study of theoretical properties of the new algorithm, and make connections with some existing algorithms. Finally, the sampler is adapted for application under the challenging "likelihood free," approximate Bayesian computation modelling framework, where we demonstrate superior performance over existing likelihood-free samplers.

References in corpus (4)

• Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems
• Central limit theorem for sequential Monte Carlo methods and its application to Bayesian inference
• Approximate Bayesian Computation: a nonparametric perspective
• Adaptive methods for sequential importance sampling with application to state space models

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• Likelihood-free Bayesian inference for alpha-stable models
• Comments on "Particle Markov Chain Monte Carlo" by C. Andrieu, A. Doucet and R. Hollenstein
• Likelihood-based inference for max-stable processes