Presentation	2002/3/11 Upper Bounds of Stochastic Complexity in Mixture Models Keisuke YAMAZAKI, Sumio WATANABE,
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Abstract(in English)	It is well known that mixture models such as gaussian mixtures are non-regular statistical models, since the set of parameters of small size models is an analystic set with singularities in the space of parameters of a large size models. Because of these singularities, the mathematical foundation of the models is not yet constructed though the models are applied in a lot of infomation processing systems. Recent years using the algebraic geometrical method, we can calculate the stochastic complexity that is the most important value to clarify the models. This paper proves the theorem that shows the upper bounds of it in mixture models with the method.
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Keyword(in English)	Mixture Models / Stochastic Complexity / Algebraic Geometry / Singularities
Paper #	NC2001-150
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Committee	NC
Conference Date	2002/3/11(1days)
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Registration To	Neurocomputing (NC)
Language	ENG
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Upper Bounds of Stochastic Complexity in Mixture Models
Sub Title (in English)
Keyword(1)	Mixture Models
Keyword(2)	Stochastic Complexity
Keyword(3)	Algebraic Geometry
Keyword(4)	Singularities
1st Author's Name	Keisuke YAMAZAKI
1st Author's Affiliation	Dept. of Advanced Applied Electronics, Tokyo Institute of Technology()
2nd Author's Name	Sumio WATANABE
2nd Author's Affiliation	P&I Lab., Tokyo Institute of Technology
Date	2002/3/11
Paper #	NC2001-150
Volume (vol)	vol.101
Number (no)	735
Page	pp.pp.-
#Pages	8
Date of Issue