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Hidden Markov Models
Section 2 - Page 1
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Uses associated with HMMs
Once a system can be described as a HMM, three problems can be
solved. The first two are pattern recognition problems: Finding
the probability of an observed sequence given a HMM
(evaluation); and finding the sequence of hidden states that
most probably generated an observed sequence (decoding). The
third problem is generating a HMM given a sequence of
observations (learning).
1. Evaluation
Consider the problem where we have a number of HMMs (that is, a
set of ( ,A,B)
triples) describing different systems, and a sequence of
observations. We may want to know which HMM most probably
generated the given sequence. For example, we may have a
`Summer' model and a `Winter' model for the seaweed, since
behaviour is likely to be different from season to season - we
may then hope to determine the season on the basis of a sequence
of dampness observations.
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