In probability & statistics, A Markov chain or Markoff chain or a Markov Process, Named after the Russian mathematician Andrey Markov, Is a stochastic process that satisfies the Markov property And is usually characterized as "memorylessness".
Imagine an urn experiment with replacement, Hidden Markov Model can be visualized likewise.
Consider a hidden room with a genie inside, The room has N urns with n ***** in each.
The genie chooses an urn in that room, He randomly draws a ball from the urn.
He then puts the ball onto a conveyor belt, Which is being observed for the sequence, Only the ***** on the conveyor are visible, Not the urns from which they were drawn.
The genie has a procedure to choose urns, The choice of the urn for the n-th ball, It depends only upon a random number, And the choice of the urn for the (n − 1)-th ball.
The choice of urn does not directly depend on The urns chosen before this single previous urn; Therefore, this is called a Markov process.
*Hidden Markov models model complex Markov processes, Where the states emit the observations according to a distribution. One such example is a Gaussian distribution, In such a Hidden Markov Model, The state's output are represented by a Gaussian distribution.
References: Wikipedia.
A Hidden Markov Model (HMM) is a statistical Markov model in which the system being modelled is assumed to be a Markov process with unobserved (hidden) states. An HMM can be presented as the simplest dynamic Bayesian network.
For revising an important topic from bioinformatics.