Probabilistic independence networks for hidden Markov probability models

Probabilistic independence networks for hidden Markov probability models (1997)

Cached

Download Links

Other Repositories/Bibliography

DBLP

by Padhraic Smyth , David Heckerman , Michael I. Jordan

Venue:	Lifestyles() • Vendor() • AssortmentDefault() • Assortment(Assortment) • ProductDetailLegcareDefault() • ProductDetailLegcare(Product) • ProductDetailLegwearDefault() • ProductDetailLegwearProduct(Product) • ProductDetailLegwearAssortment(Assortment) • Pr
Citations:	154 - 12 self

Versions

Version History

Metadata Version 1

Datum	Value	Source
TITLE	Probabilistic independence networks for hidden Markov probability models	INFERENCE
AUTHOR NAME	Padhraic Smyth	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Information and Computer Science; University of California, Irvine	SVM HeaderParse 0.2
AUTHOR ADDR	CA 92717-3425.	SVM HeaderParse 0.2
AUTHOR NAME	David Heckerman	SVM HeaderParse 0.2
AUTHOR AFFIL	Microsoft Research, Building 9S/1	SVM HeaderParse 0.2
AUTHOR ADDR	Redmond, WA 98052-6399	SVM HeaderParse 0.2
AUTHOR NAME	Michael I. Jordan	SVM HeaderParse 0.2
AUTHOR AFFIL	; Department of Brain and Cognitive Sciences; MIT, Cambridge; and with the Jet Propulsion Laboratory 525-3660, California Institute of Technology, 4800 Oak Grove	SVM HeaderParse 0.2
AUTHOR ADDR	; MA 02139	SVM HeaderParse 0.2
ABSTRACT	Graphical techniques for modeling the dependencies of random variables have been explored in a variety of di erent areas including statistics, statistical physics, arti-cial intelligence, speech recognition, image processing, and genetics. Formalisms for manipulating these models have been developed relatively independently in these research communities. In this paper we explore hidden Markov models (HMMs) and related structures within the general framework of probabilistic independence networks (PINs). The paper contains a self-contained review of the basic principles of PINs. It is shown that the well-known forward-backward (F-B) and Viterbi algorithms for HMMs are special cases of more general inference algorithms for arbitrary PINs. Furthermore, the existence of inference and estimation algorithms for more general graphical models provides a set of analysis tools for HMM practitioners who wish to explore a richer class of HMM structures. Examples of relatively complex models to handle sensor fusion and coarticulation in speech recognition are introduced and treated within the graphical model framework to illustrate the advantages of the general approach. 1	SVM HeaderParse 0.2
YEAR	1997	INFERENCE
VENUE	Lifestyles() • Vendor() • AssortmentDefault() • Assortment(Assortment) • ProductDetailLegcareDefault() • ProductDetailLegcare(Product) • ProductDetailLegwearDefault() • ProductDetailLegwearProduct(Product) • ProductDetailLegwearAssortment(Assortment) • Pr	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	49 found	ParsCit 1.0