Marginal Likelihood Integrals for Mixtures of Independence Models

	Marginal Likelihood Integrals for Mixtures of Independence Models
	Lin, Shaowei 1; Sturmfels, Bernd 1; Xu, Zhiqiang2
	2009-07-01
Source Publication	JOURNAL OF MACHINE LEARNING RESEARCH
ISSN	1532-4435
Volume	10 Pages:1611-1631
Abstract	Inference in Bayesian statistics involves the evaluation of marginal likelihood integrals. We present algebraic algorithms for computing such integrals exactly for discrete data of small sample size. Our methods apply to both uniform priors and Dirichlet priors. The underlying statistical models are mixtures of independent distributions, or, in geometric language, secant varieties of Segre-Veronese varieties.
Keyword	marginal likelihood exact integration mixture of independence model computational algebra
Language	英语
Funding Project	A*STAR (Agency for Science, Technology and Research, Singapore) ; Alexander von Humboldt research prize ; U. S. National Science Foundation[DMS-0456960] ; NSFC[10871196] ; Sofia Kovalevskaya prize
WOS Research Area	Automation & Control Systems ; Computer Science
WOS Subject	Automation & Control Systems ; Computer Science, Artificial Intelligence
WOS ID	WOS:000270825000011
Publisher	MICROTOME PUBL
Citation statistics
Document Type	期刊论文
Identifier	http://ir.amss.ac.cn/handle/2S8OKBNM/8219
Collection	计算数学与科学工程计算研究所
Corresponding Author	Lin, Shaowei
Affiliation	1.Univ Calif Berkeley, Dept Math, Berkeley, CA 94720 USA 2.Chinese Acad Sci, Acad Math & Syst Sci, LSEC, Beijing 100080, Peoples R China
Recommended Citation GB/T 7714	Lin, Shaowei,Sturmfels, Bernd,Xu, Zhiqiang. Marginal Likelihood Integrals for Mixtures of Independence Models[J]. JOURNAL OF MACHINE LEARNING RESEARCH,2009,10:1611-1631.
APA	Lin, Shaowei,Sturmfels, Bernd,&Xu, Zhiqiang.(2009).Marginal Likelihood Integrals for Mixtures of Independence Models.JOURNAL OF MACHINE LEARNING RESEARCH,10,1611-1631.
MLA	Lin, Shaowei,et al."Marginal Likelihood Integrals for Mixtures of Independence Models".JOURNAL OF MACHINE LEARNING RESEARCH 10(2009):1611-1631.

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