This paper introduces an independent-proposal Metropolis–Hastings sampler for
Bayesian probit regression. The Gibbs sampler of Albert and Chib has been the
default sampler since its introduction in 1993. We conduct a simulation study
comparing the two methods under various combinations of predictor variables and
sample sizes. The proposed sampler is shown to outperform that of Albert and Chib
in terms of efficiency measured through autocorrelation, effective sample size, and
computation time. We then demonstrate performance of the samplers on real data
applications with analogous results.
Keywords
probit regression, Markov chain Monte Carlo, Gibbs
sampling, Metropolis–Hastings
