Use the Viterbi algorithm to find the Viterbi path of a hidden Markov model given a sequence of outputs. The model assumes that the generation starts in state 1 at step 0 but does not include step 0 in the generated states and sequence.

Arguments

• sequence is the vector of length len of given outputs. The outputs must be integers ranging from 1 to columns (outprob).
• transprob is the matrix of transition probabilities of the states. transprob(i, j) is the probability of a transition to state j given state i.
• outprob is the matrix of output probabilities. outprob(i, j) is the probability of generating output j given state i.

Return values

• vpath is the vector of the same length as sequence of the estimated hidden states. The states are integers ranging from 1 to columns (transprob).

If "symbols" is specified, then sequence is expected to be a sequence of the elements of symbols instead of integers ranging from 1 to columns (outprob). symbols can be a cell array.

If "statenames" is specified, then the elements of statenames are used for the states in vpath instead of integers ranging from 1 to columns (transprob). statenames can be a cell array.

Examples

 transprob = [0.8, 0.2; 0.4, 0.6];
 outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
 [sequence, states] = hmmgenerate (25, transprob, outprob);
 vpath = hmmviterbi (sequence, transprob, outprob);
 

 symbols = {"A", "B", "C"};
 statenames = {"One", "Two"};
 [sequence, states] = hmmgenerate (25, transprob, outprob, ...
                      "symbols", symbols, "statenames", statenames);
 vpath = hmmviterbi (sequence, transprob, outprob, ...
         "symbols", symbols, "statenames", statenames);
 

References

1. Wendy L. Martinez and Angel R. Martinez. Computational Statistics Handbook with MATLAB. Appendix E, pages 547-557, Chapman & Hall/CRC, 2001.
2. Lawrence R. Rabiner. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE, 77(2), pages 257-286, February 1989.

Source Code: hmmviterbi