  We present a general probabilistic perspective on Gaussian filtering and
smoothing. This allows us to show that common approaches to Gaussian
filtering/smoothing can be distinguished solely by their methods of
computing/approximating the means and covariances of joint probabilities. This
implies that novel filters and smoothers can be derived straightforwardly by
providing methods for computing these moments. Based on this insight, we derive
the cubature Kalman smoother and propose a novel robust filtering and smoothing
algorithm based on Gibbs sampling.
