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Probabilistic Independence Networks for Hidden Markov Probability Models

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dc.contributor.author Smyth, Padhraic en_US
dc.contributor.author Heckerman, Cavid en_US
dc.contributor.author Jordan, Michael I en_US
dc.date.accessioned 2004-09-30
dc.date.available 2004-09-30
dc.date.issued 1996 en_US
dc.identifier.clearanceno 96-0287 en_US
dc.identifier.uri http://hdl.handle.net/2014/24199
dc.description.abstract 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 enference algorithms for arbitrary PINs. en_US
dc.format.extent 2629508 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.subject.other random variables pattern recognition signal processing Markov Probability Models en_US
dc.title Probabilistic Independence Networks for Hidden Markov Probability Models en_US


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