  Bayesian Student-$t$ linear regression is a common robust alternative to the
normal model, but its theoretical properties are not well understood. We aim to
fill some gaps by providing analyses in two different asymptotic scenarios. The
results allow to precisely characterize the trade-off between robustness and
efficiency controlled through the degrees of freedom (at least asymptotically).
