I know how to fit a hidden markov model to a data sequence, using the matlab-implementation of the baum-welch algorithm.
But what should I do if I do not have one data sequence, but a bunch of them? How can I fit to a set of sequences?
I think what I want to do is to maximize the likelihood of the HMM to output any of my sequences.
Is there a best practice for that kind of problem?
Best Answer
If you'd like to know the theory of doing this, it's covered in Rabiner's great paper "A tutorial to Hidden Markov models and selected applications in speech recognition" (Proc of the IEEE, 1989, 77(2), p.273; the full text available on multiple websites online - just google the name). As for whether there is an implementation in Matlab (or any other environment), I don't unfortunately know.