Summary

In this chapter, we talked about applying Bayesian learning in the case of learning parameters in HMMs. Bayesian learning has a few benefits over the maximum-likelihood estimator, but it turns out to be computationally quite expensive except when we have closed-form solutions. Closed-form solutions are only possible when we use conjugate priors. In the following chapters, we will discuss detailed applications of HMMs for a wide variety of problems.