Rao-Blackwellized Particle Smoothing as Message Passing

In this manuscript the fixed-lag smoothing problem for conditionally linear Gaussian state-space models is investigated from a factor graph perspective. More specifically, after formulating Bayesian smoothing for an arbitrary state-space model as forward-backward message passing over a factor graph, we focus on the above mentioned class of models and derive a novel Rao-Blackwellized particle smoother for it. Then, we show how our technique can be modified to estimate a point mass approximation of the so called joint smoothing distribution. Finally, the estimation accuracy and the computational requirements of our smoothing algorithms are analysed for a specific state-space model.