hidden markov model

Hidden Markov Model (HMM)

Definition:-

Hidden Markov Model (HMM) is a statistical Markov model in which the system

being modeled is assumed to be a Markov process with unobservable (i.e. hidden) states.

The hidden Markov model can be represented as the simplest dynamic Bayesian network. The mathematics behind the HMM were developed by L. E. Baum and coworkers.

In simpler Markov models (like a Markov chain), the state is directly visible to the observer, and

therefore the state transition probabilities are the only parameters, while in the hidden Markov

model, the state is not directly visible, but the output (in the form of data or "token" in the

following), dependent on the state, is visible..

Application:-

HMMs can be applied in many fields where the goal is to recover a data sequence

that is not immediately observable (but other data that depend on the sequence are).

  Applications :- 

 Single-molecule kinetic analysis

 Gene prediction

 Handwriting recognition

 Alignment of bio-sequences

 Time series analysis

 Activity recognition

 Protein folding

 Sequence classification

 DNA motif discovery

 Chromatin state discovery