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(MA 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.
把运动矢量场建模为高斯马尔科夫随机场，对丢失图像块的运动矢量采用最大后验概率方法恢复，其权值能够根据空间和时间信息而自适应选择。
The motion vectors of the damaged image macroblocks can be recovered adaptively by Maximum A Posteriori(MAP), and the weight is selected adaptively based o.
把运动矢量场建模为高斯马尔科夫随机场，对丢失图像块的运动矢量采用最大后验概率方法恢复，其权值能够根据空间和时间信息而自适应选择。
The motion vectors of the damaged image macroblocks can be recovered adaptively by Maximum A Posteriori(MAP), and the weight is selected adaptively based o.
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