  Sequential Monte Carlo (SMC) methods, such as the particle filter, are by now
one of the standard computational techniques for addressing the filtering
problem in general state-space models. However, many applications require
post-processing of data offline. In such scenarios the smoothing problem--in
which all the available data is used to compute state estimates--is of central
interest. We consider the smoothing problem for a class of conditionally linear
Gaussian models. We present a forward-backward-type Rao-Blackwellized particle
smoother (RBPS) that is able to exploit the tractable substructure present in
these models. Akin to the well known Rao-Blackwellized particle filter, the
proposed RBPS marginalizes out a conditionally tractable subset of state
variables, effectively making use of SMC only for the "intractable part" of the
model. Compared to existing RBPS, two key features of the proposed method are:
(i) it does not require structural approximations of the model, and (ii) the
aforementioned marginalization is done both in the forward direction and in the
backward direction.
