  Conventional recursive filtering approaches, designed for quantifying the
state of an evolving uncertain dynamical system with intermittent observations,
use a sequence of (i) an uncertainty propagation step followed by (ii) a step
where the associated data is assimilated using Bayes' rule. In this paper we
switch the order of the steps to: (i) one step ahead data assimilation followed
by (ii) uncertainty propagation. This route leads to a class of filtering
algorithms named \emph{smoothing filters}. For a system driven by random noise,
our proposed methods require the probability distribution of the driving noise
after the assimilation to be biased by a nonzero mean. The system noise,
conditioned on future observations, in turn pushes forward the filtering
solution in time closer to the true state and indeed helps to find a more
accurate approximate solution for the state estimation problem.
