Markov Models | Pattern Recognition Tutorial | Minigranth

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 Markovmodel, 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.