Volume 4 • Issue 1 | March 2020

Posterior Summary of Bayes Error Using Monte-Carlo Sampling and Its Application in Credit Scoring

Ha Che-Ngoc

Abstract: Bayesian classifier is one of the data classification methods that are of interest. In the Bayesian classifier, Bayes error, Pe is an important measure because it can estimate the error of the model built through the calculation of the posterior probability function's overlapping area. The exact calculation of Pe depends on the exact calculation of likelihood functions and the prior probability of each type. In previous studies, the prior probability has been considered as a fixed value only, hence, the Bayes error is usually a fixed value. This sometimes leads to unreasonable results. To fill the mentioned research gap, this paper considers the prior probability q in Bayesian classifier as a distribution, and looks insight the posterior distribution of Bayes error, using Monte-Carlo simulation. Finally, the proposed method is applied to credit scoring data of a bank in Vietnam. Based on the results, we can determine whether the Bayesian classifier is suitable for data or not. In addition, the prior parameter setting can be tested through sensitivity analysis.