An Overview of Mixture Models
Abstract
This paper has been withdrawn. With the advancement of statistical theory and computing power, data sets are providing a greater amount of insight into the problems of today. Statisticians have an ever increasing number of tools to attack these problems, some of which can be implemented in the area of mixture modeling. There is a great deal of literature on mixture models and this work attempts to provide a general overview of the subject, including the discussion of relevant issues and algorithms. The reader can hope to gain a broad understanding of concepts in mixture modeling and find the references cited within as a valuable resource for the next stage of their research.
Cite
@article{arxiv.0808.0383,
title = {An Overview of Mixture Models},
author = {Derek S. Young},
journal= {arXiv preprint arXiv:0808.0383},
year = {2012}
}
Comments
Portions of this review article need to be enhanced with further explanation. Some of this material will be incorporated into a future manuscript