Improving SMC sampler estimate by recycling all past simulated particles

Sequential Monte Carlo (SMC) methods are not only a popular tool in the analysis of state-space models, but offer a powerful alternative to Markov chain Monte Carlo (MCMC) in situations where static Bayesian inference must be performed via simulation. In this paper, we propose a recycling scheme of all past simulated particles in the SMC sampler in order to reduce the variance of the final estimator. We demonstrate how the proposed approach outperforms the classical strategy in two challenging models.