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Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7185
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dc.creatorSmyth, Padhraic-
dc.creatorHeckerman, David-
dc.creatorJordan, Michael-
dc.date2004-10-20T20:49:09Z-
dc.date2004-10-20T20:49:09Z-
dc.date1996-03-13-
dc.date.accessioned2013-10-09T02:48:29Z-
dc.date.available2013-10-09T02:48:29Z-
dc.date.issued2013-10-09-
dc.identifierAIM-1565-
dc.identifierCBCL-132-
dc.identifierhttp://hdl.handle.net/1721.1/7185-
dc.identifier.urihttp://koha.mediu.edu.my:8181/xmlui/handle/1721-
dc.descriptionGraphical techniques for modeling the dependencies of randomvariables have been explored in a variety of different areas includingstatistics, statistical physics, artificial intelligence, speech recognition, image processing, and genetics.Formalisms for manipulating these models have been developedrelatively independently in these research communities. In this paper weexplore hidden Markov models (HMMs) and related structures within the general framework of probabilistic independencenetworks (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 Viterbialgorithms for HMMs are special cases of more general inference algorithms forarbitrary PINs. Furthermore, the existence of inference and estimationalgorithms for more general graphical models provides a set of analysistools for HMM practitioners who wish to explore a richer class of HMMstructures.Examples of relatively complex models to handle sensorfusion and coarticulationin speech recognitionare introduced and treated within the graphical model framework toillustrate the advantages of the general approach.-
dc.format31 p.-
dc.format664995 bytes-
dc.format687871 bytes-
dc.formatapplication/postscript-
dc.formatapplication/pdf-
dc.languageen_US-
dc.relationAIM-1565-
dc.relationCBCL-132-
dc.subjectAI-
dc.subjectMIT-
dc.subjectArtificial Intelligence-
dc.subjectgraphical models-
dc.subjectHidden Markov models-
dc.subjectHMM's-
dc.subjectlearning-
dc.subjectprobabilistic models-
dc.subjectspeech recognition-
dc.subjectBayesian networks-
dc.subjectbelief networks-
dc.subjectMarkov networks-
dc.subjectprobabilistic propagation-
dc.subjectinference-
dc.subjectcoarticulation-
dc.titleProbabilistic Independence Networks for Hidden Markov Probability Models-
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