Open Access Peer-reviewed

Some Algorithms for Large Hidden Markov Models

Sanaa Chafik1,, Daoui Cherki1

1University Sultan Moulay Slimane, Laboratory of modelisation and calcul, Béni Mellal

World Journal Control Science and Engineering. 2013, 1(1), 9-14. DOI: 10.12691/wjcse-1-1-2
Published online: August 25, 2017


The Hidden Markov Model (HMM) has become increasingly popular in the last several years because it is used in a wide range of applications. There are some inherent limitations of this type of statistical model. The major limitation of HMM is large hidden state space, which limits their practical purview. The objective of this work is to reduce the task of solving some classical algorithms (Forward, Backward, Baum-Welch) by review of their theoretical aspects, offering faster improved algorithms based on the decomposition technique which represent a general approach to solving a problem by breaking it up into smaller ones and solving each of the smaller ones separately.


Hidden Markov Model, Forward, Backward, Baum Welch, large hidden state space, divide and conquer, decomposition, communicating class, graph theory
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