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.