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, HMM 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 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.