An Introduction : Markov Model
In the field of computing, 'pattern recognition' is a multi-facet instrument to achieve distinct goals.Be it the field of Gesture- Speech-Handwriting recognition or Statistical Software Testing or Software Reliability prediction it has always proved the most concrete method to empower the field of Artificial Intelligence.
Markov model is the most exploited technique for 'pattern recognition'. The core principle of Markov model "the future is independent of the past , given the present" highlights the basic feature of it. A Markov Model , for any selected system , is the relationship among all possible states of the system.This relationship is basically depicted through the transition paths, rate of transition between those states and the probability of the system being in any of those state.And with this relationship modeling the next state of the system can be predicted.
For an example System XYZ can be either in ON or OFF state then the markov model will be :
Here the λ denotes the rate of transition from ON state to OFF state.So the probability of the system to be in ON and OFF state can be denoted as P_ON and P_OFF respectively.And these probabilities decides the prediction of the future states.
Markov Model has already proven its' utility in the field of gesture recognition, speech recognition , handwriting recognition , statistical software testing , predicting the reliability of the system ,and other numerous applications. So Markov Model can be a promising modeling technique to enhance the existing technologies.
With the increasing complexity of systems and inability of Markov model to model all aspects of the system , more sophisticated and exhaustive models like Hidden markov model , Hierarichal markov model and Logical markov model etc are created. This clearly reflects the extendible nature of Markov model.