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dc.contributor.authorSmyth, Padhraicen_US
dc.contributor.authorHeckerman, Daviden_US
dc.contributor.authorJordan, Michaelen_US
dc.date.accessioned2004-10-20T20:49:09Z
dc.date.available2004-10-20T20:49:09Z
dc.date.issued1996-03-13en_US
dc.identifier.otherAIM-1565en_US
dc.identifier.otherCBCL-132en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/7185
dc.description.abstractGraphical 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.en_US
dc.format.extent31 p.en_US
dc.format.extent664995 bytes
dc.format.extent687871 bytes
dc.format.mimetypeapplication/postscript
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.relation.ispartofseriesAIM-1565en_US
dc.relation.ispartofseriesCBCL-132en_US
dc.subjectAIen_US
dc.subjectMITen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectgraphical modelsen_US
dc.subjectHidden Markov modelsen_US
dc.subjectHMM'sen_US
dc.subjectlearningen_US
dc.subjectprobabilistic modelsen_US
dc.subjectspeech recognitionen_US
dc.subjectBayesian networksen_US
dc.subjectbelief networksen_US
dc.subjectMarkov networksen_US
dc.subjectprobabilistic propagationen_US
dc.subjectinferenceen_US
dc.subjectcoarticulationen_US
dc.titleProbabilistic Independence Networks for Hidden Markov Probability Modelsen_US


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