Probabilistic Independence Networks for Hidden Markov Probability Models

Probabilistic Independence Networks for Hidden Markov Probability Models

Graphical 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 dev...

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Bibliographic Details
Main Authors: Smyth, Padhraic, Heckerman, David, Jordan, Michael
Language:en_US
Published: 2004
Subjects:
AI
MIT
Artificial Intelligence
graphical models
Hidden Markov models
HMM's
learning
probabilistic models
speech recognition
Bayesian networks
belief networks
Markov networks
probabilistic propagation
inference
coarticulation
Online Access:http://hdl.handle.net/1721.1/7185

Internet

http://hdl.handle.net/1721.1/7185

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