Replica Conditional Sequential Monte Carlo

Abstract

We propose a Markov chain Monte Carlo (MCMC) scheme to perform state inference in non-linear non-Gaussian state-space models. Current state-of-the-art methods to address this problem rely on particle MCMC techniques and its variants, such as the iterated conditional Sequential Monte Carlo (cSMC) scheme, which uses a Sequential Monte Carlo (SMC) type proposal within MCMC. A deficiency of standard SMC proposals is that they only use observations up to time $t$ to propose states at time $t$ when an entire observation sequence is available. More sophisticated SMC based on lookahead techniques could be used but they can be difficult to put in practice. We propose here replica cSMC where we build SMC proposals for one replica using information from the entire observation sequence by conditioning on the states of the other replicas. This approach is easily parallelizable and we demonstrate its excellent empirical performance when compared to the standard iterated cSMC scheme at fixed computational complexity.

arXiv commentTo appear in Proceedings of ICML '19

References in corpus (4)

• Lookahead Strategies for Sequential Monte Carlo
• MCMC Using Ensembles of States for Problems with Fast and Slow Variables such as Gaussian Process Regression
• Particle Smoothing for Hidden Diffusion Processes: Adaptive Path Integral Smoother
• On embedded hidden Markov models and particle Markov chain Monte Carlo methods