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# 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.

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.