Patterns generated by a hidden process
Section 3 - Page 1
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Summary
We have seen that there are some processes where an observed
sequence is probabalistically related to an underlying Markov
process. In such cases, the number of observable states may be
different to the number of hidden states.
We model such cases using a hidden Markov model (HMM). This is a
model containing two sets of states and three sets of
probabilities;
- hidden states : the (TRUE) states of a system that
may be described by a Markov process (e.g., the weather).
- observable states : the states of the process that
are `visible' (e.g., seaweed dampness).
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