Spike and slab regression with a variety of residual error
distributions corresponding to Gaussian, Student T, probit, logit, SVM, and a
few others. Spike and slab regression is Bayesian regression with prior
distributions containing a point mass at zero. The posterior updates the
amount of mass on this point, leading to a posterior distribution that is
actually sparse, in the sense that if you sample from it many coefficients are
actually zeros. Sampling from this posterior distribution is an elegant way
to handle Bayesian variable selection and model averaging. See
for an explanation of the Gaussian case.