Markov property
Memoryless property of a stochastic process / From Wikipedia, the free encyclopedia
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In probability theory and statistics, the term Markov property refers to the memoryless property of a stochastic process, which means that its future evolution is independent of its history. It is named after the Russian mathematician Andrey Markov.[1] The term strong Markov property is similar to the Markov property, except that the meaning of "present" is defined in terms of a random variable known as a stopping time.
The term Markov assumption is used to describe a model where the Markov property is assumed to hold, such as a hidden Markov model.
A Markov random field extends this property to two or more dimensions or to random variables defined for an interconnected network of items.[2] An example of a model for such a field is the Ising model.
A discrete-time stochastic process satisfying the Markov property is known as a Markov chain.