  We perform a Bayesian analysis of the p-variate skew-t model, providing a new
parameterization, a set of non-informative priors and a sampler specifically
designed to explore the posterior density of the model parameters. Extensions,
such as the multivariate regression model with skewed errors and the stochastic
frontiers model, are easily accommodated. A novelty introduced in the paper is
given by the extension of the bivariate skew-normal model given in Liseo &
Parisi (2013) to a more realistic p-variate skew-t model. We also introduce the
R package mvst, which allows to estimate the multivariate skew-t model.
