Equivalence and Reduction of Hidden Markov Models

dc.creator	Balasubramanian, Vijay
dc.date	2004-10-20T19:55:31Z
dc.date	2004-10-20T19:55:31Z
dc.date	1993-01-01
dc.date.accessioned	2013-10-09T02:46:58Z
dc.date.available	2013-10-09T02:46:58Z
dc.date.issued	2013-10-09
dc.identifier	AITR-1370
dc.identifier	http://hdl.handle.net/1721.1/6801
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description	This report studies when and why two Hidden Markov Models (HMMs) may represent the same stochastic process. HMMs are characterized in terms of equivalence classes whose elements represent identical stochastic processes. This characterization yields polynomial time algorithms to detect equivalent HMMs. We also find fast algorithms to reduce HMMs to essentially unique and minimal canonical representations. The reduction to a canonical form leads to the definition of 'Generalized Markov Models' which are essentially HMMs without the positivity constraint on their parameters. We discuss how this generalization can yield more parsimonious representations of stochastic processes at the cost of the probabilistic interpretation of the model parameters.
dc.format	111 p.
dc.format	339883 bytes
dc.format	1337526 bytes
dc.format	application/octet-stream
dc.format	application/pdf
dc.language	en_US
dc.relation	AITR-1370
dc.subject	Hideen Markov Models
dc.subject	minimazation
dc.subject	statistical modelling
dc.subject	sstochastic processes
dc.title	Equivalence and Reduction of Hidden Markov Models

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