Markov Model

Pattern Recognition Tutorial


Markov Model : Introduction

  • Markov model is an un-precised model that is used in the systems that does not have any fixed patterns of occurrence  i.e. randomly changing systems.

  • Markov model is based upon the fact of having a random probability distribution or pattern that may be analysed statistically but cannot be predicted precisely.

  • In Markov model, it is assumed that the future states only depends upon the current states and not the previously occurred states.

  • There are four common Markov-Models out of which the most commonly used is the hidden Markov-Model.


This image describes the various types of Markov model present in the field pattern recognition out of which hidden Markov model is most common.

Markov Model : Types


Hidden Markov Model(HMM)

  • Hidden Markov-Model is an temporal probabilistic model for which a single discontinuous random variable determines all the states of the system.

  • It means that, possible values of variable = Possible states in the system.

  • For example: Sunlight can be the variable and sun can be the only possible state.

  • The structure of Hidden Markov-Model is restricted to the fact that basic algorithms can be implemented using matrix representations.


Concept : Hidden Markov Model

  • In Hidden Markov-Model, every individual states has limited number of transitions and emissions.

  • Probability is assigned for each transition between states.

  • Hence, the past states are totally independent of future states.

  • The fact that HMM is called hidden because of its ability of being a memory less process i.e. its future and past states are not dependent on each other.

  • Since, Hidden Markov-Model is rich in mathematical structure it can be implemented for practical applications.
  • This can be achieved on two algorithms called as:

    1. Forward Algorithm.

    2. Backward Algorithm.


Applications : Hidden Markov Model

  • Speech Recognition.

  • Gesture Recognition.

  • Language Recognition.

  • Motion Sensing and Analysis.

  • Protein Folding.