On the Bernoulli Mixture Model for Bidikmisi Scholarship Classification with Bayesian MCMC

Abstract

This research has a purpose to develop Bernoulli Mixture model for Bidikmisi data modelling using Bayesian approach. Model development is done by considering the specificity in the data acceptance of Bidikmisi scholarship prototype in East Java Province. Bidikmisi acceptance status having a binary type (0 and 1) coupled with the main criteria factor of parent income and the number of dependents family produces a structure of Bernoulli mixture distribution with two components. The characteristics of each component can be identified through the Bernoulli Mixture modelling by involving the covariates of Bidikmisi scholarship recipients. The estimating parameter was performed using Bayesian Markov Chain Monte Carlo (MCMC) couple with the Gibbs Sampling algorithm. This model is applied to data registrants Bidikmisi districts/cities in the province of East Java as many as 44,489 students. This model shows the smallest value of Deviance Information Criteria (DIC) compared with Bayesian binary logistic regression.