Runs the Maximum Likelihood learning algorithm for hidden Markov models.
Supervised learning problem. Given some training observation sequences O = {o1, o2, ..., oK}, known training state paths H = {h1, h2, ..., hK} and general structure of HMM (numbers of hidden and visible states), determine HMM parameters M = (A, B, pi) that best fit training data.