Hidden Markov Models

Simon Wimmer 🌐

May 25, 2018

This is a development version of this entry. It might change over time and is not stable. Please refer to release versions for citations.


This entry contains a formalization of hidden Markov models [3] based on Johannes Hölzl's formalization of discrete time Markov chains [1]. The basic definitions are provided and the correctness of two main (dynamic programming) algorithms for hidden Markov models is proved: the forward algorithm for computing the likelihood of an observed sequence, and the Viterbi algorithm for decoding the most probable hidden state sequence. The Viterbi algorithm is made executable including memoization. Hidden markov models have various applications in natural language processing. For an introduction see Jurafsky and Martin [2].


BSD License


Session Hidden_Markov_Models