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Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6801
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dc.creatorBalasubramanian, Vijay-
dc.date2004-10-20T19:55:31Z-
dc.date2004-10-20T19:55:31Z-
dc.date1993-01-01-
dc.date.accessioned2013-10-09T02:46:58Z-
dc.date.available2013-10-09T02:46:58Z-
dc.date.issued2013-10-09-
dc.identifierAITR-1370-
dc.identifierhttp://hdl.handle.net/1721.1/6801-
dc.identifier.urihttp://koha.mediu.edu.my:8181/xmlui/handle/1721-
dc.descriptionThis 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.format111 p.-
dc.format339883 bytes-
dc.format1337526 bytes-
dc.formatapplication/octet-stream-
dc.formatapplication/pdf-
dc.languageen_US-
dc.relationAITR-1370-
dc.subjectHideen Markov Models-
dc.subjectminimazation-
dc.subjectstatistical modelling-
dc.subjectsstochastic processes-
dc.titleEquivalence and Reduction of Hidden Markov Models-
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