Hidden Markov Models

  • David Forsyth


There are many situations where one must work with sequences. Here is a simple, and classical, example. We see a sequence of words, but the last word is missing. I will use the sequence “I had a glass of red wine with my grilled xxxx.” What is the best guess for the missing word? You could obtain one possible answer by counting word frequencies, then replacing the missing word with the most common word. This is “the,” which is not a particularly good guess because it doesn’t fit with the previous word. Instead, you could find the most common pair of words matching “grilled xxxx,” and then choose the second word. If you do this experiment (I used Google Ngram viewer, and searched for “grilled *”), you will find mostly quite sensible suggestions (I got “meats,” “meat,” “fish,” “chicken,” in that order). If you want to produce random sequences of words, the next word should depend on some of the words you have already produced. A model with this property that is very easy to handle is a Markov chain (defined below).

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • David Forsyth
    • 1
  1. 1.Computer Science DepartmentUniversity of Illinois Urbana ChampaignUrbanaUSA

Personalised recommendations