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 RabinerTutorial10.1.1.131.2084.pdf (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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