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HIDDEN MARKOV MODEL MATLAB

10/25/2018

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E = [ 1 6 1 6 1 6 1 6 1 6 1 6 7 12 1 12 1 12 1 12 1 12 1 12 ] The model is not
hidden because you know the sequence of states from the colors of the coins and
dice. Suppose, however, that someone else is generating the emissions without
showing you the dice or the coins. All you see is the sequence of emissions. If
you start seeing more 1s than other numbers, you might suspect that the model is
in the green state, but you cannot be sure because you cannot see the color of
the die being rolled. Hidden Markov models raise the following questions.

• — Generates a sequence of states and emissions from a Markov model • —
Calculates maximum likelihood estimates of transition and emission probabilities
from a sequence of emissions and a known sequence of states • — Calculates
maximum likelihood estimates of transition and emission probabilities from a
sequence of emissions • — Calculates the most probable state path for a hidden
Markov model • — Calculates the posterior state probabilities of a sequence of
emissions This section shows how to use these functions to analyze hidden Markov
models. Generating a Test Sequence The following commands create the transition
and emission matrices for the model described in the. [seq,states] =
hmmgenerate(1000,TRANS,EMIS); The output seq is the sequence of emissions and
the output states is the sequence of states. Hmmgenerate begins in state 1 at
step 0, makes the transition to state i 1 at step 1, and returns i 1 as the
first entry in states. To change the initial state, see. Estimating the State
Sequence Given the transition and emission matrices TRANS and EMIS, the function
uses the Viterbi algorithm to compute the most likely sequence of states the
model would go through to generate a given sequence seq of emissions.
Sum(states==likelystates)/1000 ans = 0.8200 In this case, the most likely
sequence of states agrees with the random sequence 82% of the time.


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