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.
方法：根据马尔科夫随机场图像模型，利用最大后验概率准则(M A P)，提出一种迭代松弛分割算法。
Methods: Based on Markov random fields model of noise, a iteration algorithm was presented by using maximum a posteriori (MAP) criterion.
根据条件随机场模型和马尔可夫随机场模型建立了一个最大后验概率框架。
Firstly, maximum a posteriori framework is created according to conditional random field model and Markov random field model.
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