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.Comment: To appear in Proceedings of ICML '1
