Hidden Markov Model(HMM) : Introduction

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.
Hidden Markov Model : The Concept

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:

Forward Algorithm.

Backward Algorithm.

Applications : Hidden Markov Model

Speech Recognition.

Gesture Recognition.

Language Recognition.

Motion Sensing and Analysis.

Protein Folding.
Markov Model

Markov model is an unprecised 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.