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Hidden Markov Models
Section 2 - Page 4
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3. Learning
Generating a HMM from a sequence of
obersvations
The third, and much the hardest, problem associated with HMMs is
to take a sequence of observations (from a known set), known to
represent a set of hidden states, and fit the most probable HMM;
that is, determine the ( ,A,B) triple that most probably describes what is seen.
The forward-backward algorithm is of use when the matrices A and
B are not directly (empirically) measurable, as is very often
the case in real applications.
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