DocumentCode
2054704
Title
On maximum likelihood estimation and divergence minimization
Author
Chan, Terence H. ; Yeung, Raymond W.
Author_Institution
Dept. of Electr. & Comput. Eng., Toronto Univ., Ont., Canada
fYear
2002
fDate
2002
Firstpage
158
Abstract
We reformulate probabilistic inference problems as divergence minimization (DM) problems, which have a natural geometrical interpretation. An iterative algorithm is proposed to solve the DM problem. It turns out that the EM algorithm could be regarded as a special case.
Keywords
inference mechanisms; iterative methods; maximum likelihood estimation; minimisation; probability; EM algorithm; MLE; divergence minimization; divergence minimization problems; geometrical interpretation; iterative algorithm; maximum likelihood estimation; probabilistic inference problems; Delta modulation; Inference algorithms; Information geometry; Iterative algorithms; Maximum likelihood estimation; Minimization methods; Parametric statistics; Random variables; Solid modeling;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Theory, 2002. Proceedings. 2002 IEEE International Symposium on
Print_ISBN
0-7803-7501-7
Type
conf
DOI
10.1109/ISIT.2002.1023430
Filename
1023430
Link To Document