  We study probit regression from a Bayesian perspective and give an
alternative form for the posterior distribution when the prior distribution for
the regression parameters is the uniform distribution. This new form allows
simple Monte Carlo simulation of the posterior as opposed to MCMC simulation
studied in much of the literature and may therefore be more efficient
computationally. We also provide alternative explicit expression for the first
and second moments. Additionally we provide analogous results for Gaussian
priors.
