论文提出了一种计算有限状态离散时间马尔科夫链平稳分布的算法。
An algorithm for computing the stationary distribution of finite state discrete time Markov Chains is provided in the paper.
该模型的核心部分是根据观测到的资料，通过蒙特卡洛马尔科夫链随机抽样的方法来估计变点位置的后验概率分布。
Given the observed hydrological data, the model can estimate the posterior probability distribution of each location of change-point by using the Monte Carlo Markov Chain (MCMC) sampling method.
通过求得两种随机变量的联合分布的表达式，证明了此类随机变量序列是强平稳的齐次马尔科夫链。
Then proves that this kind of random variables are the Markov chains with strong placidity by getting two kinds of expressions of the random variables combine distributing.
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