  The problem of Bayesian filtering and smoothing in nonlinear models with
additive noise is an active area of research. Classical Taylor series as well
as more recent sigma-point based methods are two well-known strategies to deal
with these problems. However, these methods are inherently sequential and do
not in their standard formulation allow for parallelization in the time domain.
In this paper, we present a set of parallel formulas that replace the existing
sequential ones in order to achieve lower time (span) complexity. Our
experimental results done with a graphics processing unit (GPU) illustrate the
efficiency of the proposed methods over their sequential counterparts.
