What is "markov process"

Markov process \Mark"ov pro`cess\, n. [after A. A. Markov, Russian mathematician, b. 1856, d. 1922.] (Statistics) a random process in which the probabilities of states in a series depend only on the properties of the immediately preceding state or the next preceeding state, independent of the path by which the preceding state was reached. It is distinguished from a Markov chain in that the states of a Markov process may be continuous as well as discrete. [Also spelled Markoff process.]

In probability theory and statistics, a Markov process or Markoff process, named after the Russian mathematician Andrey Markov, is a stochastic process that satisfies the Markov property. A Markov process can be thought of as 'memoryless': loosely speaking, a process satisfies the Markov property if one can make predictions for the future of the process based solely on its present state just as well as one could knowing the process's full history. i.e., conditional on the present state of the system, its future and past are independent.