rameshkumar7777 / markovprocess

Markov Process

Aim :

Software required :

Python

Theory:

Markov chains, named after Andrey Markov, a stochastic model that depicts a sequence of possible events where predictions or probabilities for the next state are based solely on its previous event state, not the states before. In simple words, the probability that n+1th steps will be x depends only on the nth steps not the complete sequence of steps that came before n. This property is known as Markov Property or Memorylessness.

Assumptions for Markov Chain :

1. The statistical system contains a finite number of states.
2. The states are mutually exclusive and collectively exhaustive.
3. The transition probability from one state to another state is constant over time.

Procedure :

Program

Results and Output :