zh-hans:马尔可夫性质（英语：Markov property）是概率论中的一个概念，因为俄国数学家安德雷·马可夫得名。当一个随机过程在给定现在状态及所有过去状态情况下，其未来状态的条件概率分布仅依赖于当前状态；换句话说，在给定现在状态时，它与过去状态（即该过程的历史路径）是条件独立的，那幺此随机过程即具有马尔可夫性质。具有马尔可夫性质的过程通常称之为马尔可夫过程。

数学上，如果为一个随机过程，则马尔可夫性质就是指

In probability theory and statistics, the term Markov property refers to the memoryless property of a stochastic process. It is named after the Russian mathematician Andrey Markov.

A stochastic process has the Markov property if the conditional probability distribution of future states of the process (conditional on both past and present states) depends only upon the present state, not on the sequence of events that preceded it. A process with this property is called a Markov process. 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. Both the terms "Markov property" and "strong Markov property" have been used in connection with a particular "memoryless" property of the exponential distribution.