  Filtering and smoothing algorithms for linear discrete-time state-space
models with skew-t distributed measurement noise are presented. The proposed
algorithms improve upon our earlier proposed filter and smoother using the mean
field variational Bayes approximation of the posterior distribution to a skew-t
likelihood and normal prior. Our simulations show that the proposed variational
Bayes approximation gives a more accurate approximation of the posterior
covariance matrix than our earlier proposed method. Furthermore, the novel
filter and smoother outperform our earlier proposed methods and conventional
low complexity alternatives in accuracy and speed.
