Probabilistic Independence Networks for Hidden Markov Probability Models

Smyth, Padhraic; Heckerman, Cavid; Jordan, Michael I

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