Table of Contents Introduction The Markov Assumption Definition Evaluation The Viterbi Algorithm Estimating Parameters Expectation Maximization Introduction This article is essentially a grok of a tutorial on HMMs by (RABINER 1989). It will be useful for the reader to reference the original paper.
Up to this point, we have only explored “atomic” data points. That is, all of the information about a particular sample is encapsulated into one vector. Sequential data is easily represented by graphical models. This article introduces Hidden Markov Models, a powerful probabilistic graphical model used in many applications from gesture recognition to natural language processing.