Having a basic understanding of MLE, we can now move on to applying these concepts to the case of HMMs. In the next few subsections, we will see two possible scenarios of learning in HMMs, namely, supervised learning and unsupervised learning.
MLE for HMMs
Supervised learning
In the case of supervised learning, we use the data generated by sampling the process that we are trying to model. If we are trying to parameterize our HMM model using simple discrete distributions, we can simply apply the MLE to compute the transition and emission distributions by counting the number of transitions from any given state to another state. Similarly, we can compute the emission distribution by counting the output states from different hidden...
