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Page history last edited by mike@mbowles.com 10 years, 9 months ago

Reference material for hidden Markov models. 


In class we'll use Andrew Moore's slides from cmu.  MooreSlideshmm14.pdf  and a paper put together David Harte to support the R package HiddenMarkov  HMPackageNotes.pdf .  


In addition to these there are several references you might find helpful.  The first is a tutorial by Lawrence Rabiner RabinerTutorial10. (a very well-known figure in information theory and signal processing, coding, etc.).  The next is the original paper describing the "Baum Welch" algorithm for implementing EM for hidden Markov models baum.pdf .  The last is leroux-1992.pdf who considers maximum likelihood estimation for hmm and develops rigorous conditions for things like identifiability. 

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