Assessing a mixture model for clustering with the integrated completed likelihood
Assessing a mixture model for clustering with the integrated completed likelihood is a scholarly work, published in 2000. The main subjects of the publication include mathematics, maximum a posteriori estimation, cluster analysis, data mining, Bayesian information criterion, missing data, data modeling, computer science, a priori and a posteriori, maximum likelihood estimation, pattern recognition, mixture model, Real world data, Bayesian probability, statistics, and artificial intelligence. The authors propose an assessing method of mixture model in a cluster analysis setting with integrated completed likelihood.