[KSCI] Korea Science Citation Index Service

PERFORMANCE EVALUATION OF INFORMATION CRITERIA FOR THE NAIVE-BAYES MODEL IN THE CASE OF LATENT CLASS ANALYSIS: A MONTE CARLO STUDY

Dias, Jose G. (Department of Quantitative Methods and GIESTA-UNIDE, Higher Institute of Social Sciences and Business Studies-ISCTE)

Publication Information

Journal of the Korean Statistical Society / v.36, no.3, 2007 , pp. 435-445 More about this Journal

Abstract

This paper addresses for the first time the use of complete data information criteria in unsupervised learning of the Naive-Bayes model. A Monte Carlo study sets a large experimental design to assess these criteria, unusual in the Bayesian network literature. The simulation results show that complete data information criteria underperforms the Bayesian information criterion (BIC) for these Bayesian networks.

Keywords

Bayesian information criterion (BIC); complete data information criteria; latent class model; Monte Carlo simulation; Naive-Bayes model.;

Citations & Related Records

Times Cited By Web Of Science : 0 (Related Records In Web of Science)

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