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I'm trying to find the most probable path (i.e., sequence of states) on an hidden Markov model (HMM) using the Viterbi algorithm. However, I don't know the transition and emission matrices, which I need to estimate from the observations (data).

To estimate these matrices, which algorithm should I use: the Baum-Welch algorithm or the Viterbi Training algorithm? Why?

In case I should use the Viterbi training algorithm, can anyone provide me a good pseudocode (it's not easy to find) ?

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  • $\begingroup$ The Viterbi algorithm requires the transition matrices to be known beforehand. You cannot use it (unless I am completely mistaken) a priori. To compute the transition probabilities you can use Baum-Welch (or other EM-based algorithms). $\endgroup$ Nov 14, 2012 at 13:58
  • $\begingroup$ I know the Viterbi algorithm is only to find the most probable path, but there seems to exist an algorithm called Viterbi Training (not easy to find info on it) which does the same as the Baum-Welch... $\endgroup$
    – dx_mrt
    Nov 15, 2012 at 8:21
  • $\begingroup$ This answer on crossvalidate.SE may be of use. I wasn't aware of viterbi-training. I have only used BW or other EM-based methods in the past. Based on the answer in the link, I think BW would be the most useful. It seems Viterbi-Training gives no guarantee on bounds. $\endgroup$ Nov 15, 2012 at 16:03
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    $\begingroup$ @NicholasMancuso, if you'd like to expand your comment into an answer, we could clean it up $\endgroup$
    – Merbs
    Nov 25, 2012 at 16:36

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From my comment:

This answer on crossvalidate.SE may be of use. I wasn't aware of viterbi-training. I have only used BW or other EM-based methods in the past. Based on the answer in the link, I think BW would be the most useful. It seems Viterbi-Training gives no guarantee on bounds. However, the latter does have useful application if BW takes far too long to compute.

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