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Truncated Compound Normal with Gamma Mixture Model for Mixture Density Estimation

by S. Viziananda Row, K. Srinivasa Rao, P. Srinivasa Rao
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 157 - Number 3
Year of Publication: 2017
Authors: S. Viziananda Row, K. Srinivasa Rao, P. Srinivasa Rao
10.5120/ijca2017912643

S. Viziananda Row, K. Srinivasa Rao, P. Srinivasa Rao . Truncated Compound Normal with Gamma Mixture Model for Mixture Density Estimation. International Journal of Computer Applications. 157, 3 ( Jan 2017), 6-12. DOI=10.5120/ijca2017912643

@article{ 10.5120/ijca2017912643,
author = { S. Viziananda Row, K. Srinivasa Rao, P. Srinivasa Rao },
title = { Truncated Compound Normal with Gamma Mixture Model for Mixture Density Estimation },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2017 },
volume = { 157 },
number = { 3 },
month = { Jan },
year = { 2017 },
issn = { 0975-8887 },
pages = { 6-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume157/number3/26809-2017912643/ },
doi = { 10.5120/ijca2017912643 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:02:55.575869+05:30
%A S. Viziananda Row
%A K. Srinivasa Rao
%A P. Srinivasa Rao
%T Truncated Compound Normal with Gamma Mixture Model for Mixture Density Estimation
%J International Journal of Computer Applications
%@ 0975-8887
%V 157
%N 3
%P 6-12
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, the truncated compound normal with gamma distribution model is formally presented and its density function has been derived for defining a mixture model(TCNGM) based on this as an extension work to the proposed compound normal with gamma mixture(CNGM) model introduced in our earlier work for image segmentation. Update equations for this model have been derived in the context of maximum likelihood estimation(MLE) procedure under Expectation Maximization(EM) framework.

References
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Index Terms

Computer Science
Information Sciences

Keywords

TCNGM CNGM NM GM MLE EM

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