The universe of Markov models is vast and encompasses computational concepts such as the Markov decision process, discrete Markov, Markov chain Monte Carlo for Bayesian networks, and hidden Markov models.

Markov processes, and more specifically, the hidden Markov model (HMM), are commonly used in speech recognition, language translation, text classification, document tagging, and data compression and decoding.

The first section of this chapter introduces and describes the hidden Markov model with the full implementation of the three canonical forms of the hidden Markov model using Scala. This section details the different dynamic programming techniques used in the evaluation, decoding, and training of the hidden Markov model. The design of the classifier follows the same pattern as the logistic and linear regression.

The second and last section of the chapter is dedicated to a discriminative (labels conditional to observation) alternative to the...