Backward sequential Monte Carlo for marginal smoothing

In this paper we propose a new type of particle smoother with linear computational complexity. The smoother is based on running a sequential Monte Carlo sampler backward in time after an initial forward filtering pass. While this introduces dependencies among the backward trajectories we show through simulation studies that the new smoother can outperform existing forward-backward particle smoothers when targeting the marginal smoothing densities.