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.

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在模型估计上，采用等级似然估计方法，从而避免了求后验分布的积分运算，简化了估计过程。

Using hierarchical likelihood approach, the multidimensional integral is avoided, and the hierarchical likelihood function and the process of estimating model ar.

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依据这一模型，该方法使用贝叶斯理论和领域约束获得了区域和边界的最大后验概率估计。

The method is to derive the maximum a posteriori estimate of the regions and the boundaries by using Bayesian inference and neighborhood constraints based on Markov random fields(MRFs) models.

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