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Markov processes form an important class of stochastic processes and have applications in many areas. For example, they are the basis for a general stochastic simulation method known as Markov chain Monte Carlo, which is used for simulating random objects with specific probability distributions, and has found application in Bayesian statistics.

Markov processes form an important class of stochastic processes and have applications in many areas. For example, they are the basis for a general stochastic simulation method known as Markov chain Monte Carlo, which is used for simulating random objects with specific probability distributions, and has found application in Bayesian statistics.

'''<font color="#ff8000"> 马尔可夫过程Markov processes</font>'''是一类重要的随机过程，在许多领域有着广泛的应用。例如，它们是一种通用的随机模拟方法的基础，这种方法被称为'''<font color="#ff8000"> 专业名词＋对应英文马尔科夫蒙特卡洛模拟法Markov chain MonteCarlo</font>'''，用于模拟具有特定概率分布的随机目标，并已在贝叶斯统计中得到应用。

'''<font color="#ff8000"> 马尔可夫过程Markov processes</font>'''是一类重要的随机过程，在许多领域有着广泛的应用。例如，它们是一种通用的随机模拟方法的基础，这种方法被称为'''<font color="#ff8000"> 马尔科夫蒙特卡洛模拟法Markov chain MonteCarlo</font>'''，用于模拟具有特定概率分布的随机目标，并已在贝叶斯统计中得到应用。

====Stationarity稳定性====

====Stationarity稳定性====