A new Monte Carlo sampling in Bayesian probit regression

Details A new Monte Carlo sampling in Bayesian probit regression A new Monte Carlo sampling in Bayesian probit regression

2012-02-20

arxiv.org/abs/1202.4339v3

Statistics

Methodology

The title was changed. William Strawderman joined as coauthor. A new MC sampling is proposed based on the theory in the previo

Scientific paper

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.

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