In this chapter, we introduced algorithms for doing parameter estimation of a given HMM model. We started by looking into the basics of MLE and then applied the concepts to HMMs. For HMM training, we looked into two different scenarios: supervised training, when we have the observations for the hidden states, and unsupervised training, when we only have the output observations.
We also talked about the problems with estimation using MLE. In the next chapter, we will introduce algorithms for doing parameter estimation using the Bayesian approach, which tries to solve these issues.
