Accession Number:

ADA454825

Title:

Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering

Descriptive Note:

Technical rept. no. 486

Corporate Author:

WASHINGTON UNIV SEATTLE DEPT OF STATISTICS

Personal Author(s):

Report Date:

2005-08-04

Pagination or Media Count:

57.0

Abstract:

Normal mixture models are widely used for statistical modeling of data, including cluster analysis. However maximum likelihood estimation MLE for normal mixtures using the EM algorithm may fail as the result of singularities or degeneracies. To avoid this, we propose replacing the MLE by a maximum a posteriori MAP estimator, also found by the EM algorithm. For choosing the number of components and the model parameterization, we propose a modified version of BIC, where the likelihood is evaluated at the MAP instead of the MLE. We use a highly dispersed proper conjugate prior, containing a small fraction of one observations worth of information. The resulting method avoids degeneracies and singularities, but when these are not present it gives similar results to the standard method using MLE, EM and BIC.

Subject Categories:

  • Statistics and Probability
  • Operations Research

Distribution Statement:

APPROVED FOR PUBLIC RELEASE