HMMs - Summary
Section 1 - Page 2
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- Decoding : what sequence of hidden (underlying) states
most probably generated a given sequence of observations. The
Viterbi algorithm solves this problem efficiently.
- Learning : what model most probably underlies a given
sample of observation sequences - that is, what are the
parameters of such a model. This problem may be solved by
using the forward-backward algorithm.
HMMs have proved to be of great value in analysing real systems;
their usual drawback is the over-simplification associated with
the Markov assumption - that a state is dependent only on
predecessors, and that this dependence is time independent.
A full exposition on HMMs may be found in:
L R Rabiner and B H Juang, `An introduction to HMMs', iEEE ASSP
Magazine, 3, 4-16.
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