在概率论及统计学中，马可夫过程（英语：Markov process）是一个具备了马可夫性质的随机过程，因为俄国数学家安德雷·马可夫得名。马可夫过程是不具备记忆特质的（memorylessness）。换言之，马可夫过程的条件概率仅仅与系统的当前状态相关，而与它的过去历史或未来状态，都是独立、不相关的。

具备离散状态的马可夫过程，通常被称为马可夫链。马可夫链通常使用离散的时间集合定义，又称离散时间马可夫链。有些学者虽然采用这个术语，但允许时间可以取连续的值。

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.