In an effort to understand machine learning, at least to some degree, I've been implementing the various algorithms to solve the three problems in a Hidden Markov Model. I've been using Rabiner's tutorial paper as a guide.

I have had some trouble making the Baum-Welch algorithm work - namely, the probability after re-estimation is occasionally lower than before. The paper states that the result of a Baum-Welch step is either a critical point, or more likely than the previous step. This means my implementation is incorrect.

For the purposes of debugging, I would like to know if this property is true for each variable (e.g. the initial distribution, transition matrix and emission matrix) in isolation, or only when they all are applied. I tried to follow the proof from the original Baum paper, but it was beyond my mathematical ability!


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.