Markov Chains:
Markov Chains are named after the Russian statistician A.A Markov who developed probabilistic models that are often applicable to decision making problems in business and industry associated with dynamic systems.
Markov Chains are a special case of the more general probabilistic models known as stochastic processes, in which the current state of a system depends upon all previous states. The successive future states of the Markov process are referred to as Chainsâ€”hence the name Markov Chains.
Markov Processes:
A Markov process is stochastic process in which the current state of the system depends only on the immediately preceding state of the system.