Stochastic model selection for Mixtures of Matrix-Normals

Details Stochastic model selection for Mixtures of Matrix-Normals Stochastic model selection for Mixtures of Matrix-Normals

2010-10-12

arxiv.org/abs/1010.2310v2

Statistics

Methodology

This paper has been withdrawn by the author. Some content of the work paper has been extended

Scientific paper

Finite mixtures of matrix normal distributions are a powerful tool for classifying three-way data in unsupervised problems. The distribution of each component is assumed to be a matrix variate normal density. The mixture model can be estimated through the EM algorithm under the assumption that the number of components is known and fixed. In this work we introduce, develop and explore a Bayesian analysis of the model in order to provide a tool for simultaneous model estimation and model selection. The effectiveness of the proposed method is illustrated on a simulation study and on a real example.

Affiliated with

Also associated with

No associations

Rating

Stochastic model selection for Mixtures of Matrix-Normals does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Stochastic model selection for Mixtures of Matrix-Normals, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Stochastic model selection for Mixtures of Matrix-Normals will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-608153