Dear all, I wonder if anyone can point me to an example of Baum-Welch algorithm in pymc3 that can be used to learn the parameters of a given Hidden Markov Model. I have trying to use python hmmlearn to do that but doesnt seem to be very good. The problem I am having from the following code is that…

Thank you. I will be looking into the link you sent, though my initial impression was that it includes algorithms such as forward and viterbi but not baum-welch.

You are right, it’s using forward method to better accumulate the log_prob but we dont do inference so Baum-Welch is not implemented

But it looks like the section that is labeled as ‘Semi-suprivised model’ in the notebook that you sent me the link does the same job! Am I right? all I need is to infer the parameters from the observed data, and that gives me the parameter! I am just trying to figure out how to works because I am n…

sorry to bother you again. Just to add that my impression was wrong. For ‘Semi-supervised’ model to run, one would require both observed-seqs(labeled sequences) and state-seqs whereas in my case (and for baum-welch) all I have is just observed sequences and not state sequence a priori.

You can run the unsupervised model, which is also use forward method. However, as discussed in the thread, the inference on these kind of model is extremely difficult both in pymc3 and Stan. Some EM like algorithm is needed most likely.

Baum-Welch example in PYMC3

Questions

junpenglao July 14, 2020, 7:04am 2

The closest implementation in PyMC3 compare to Stan regarding HMM is How to marginalized Hidden Markov Model with categorical?. You can try to see if it fits your use case or not.

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Baum-Welch example in PYMC3

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