In this article a new Bayesian regression model, called the Bayesian
semi-parametric logistic regression model, is introduced. This model generalizes
the semi-parametric logistic regression model (SLoRM) and improves its
estimations. This paper considers Bayesian and non-Bayesian estimation and
inference for the parametric and semi-parametric logistic regression model with
application to credit scoring data under the square error loss function. This paper
introduces a new algorithm for estimating the SLoRM parameters using Bayesian
theorem in more detail. Finally, the parametric logistic regression model
(PLoRM), the SLoRM and the Bayesian SLoRM are used and compared using a
real data set.