A new Monte Carlo sampling in Bayesian probit regression
Methodology
2012-03-15 v3
Abstract
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.
Cite
@article{arxiv.1202.4339,
title = {A new Monte Carlo sampling in Bayesian probit regression},
author = {Yuzo Maruyama and William E. Strawderman},
journal= {arXiv preprint arXiv:1202.4339},
year = {2012}
}
Comments
The title was changed. William Strawderman joined as coauthor. A new MC sampling is proposed based on the theory in the previous version