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.
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。
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.
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.
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量大后验概率估计。
Using cumulative probability to estimate the possible stage of earthquake occurrence can fulfill the origin time of Markov chain.
用累积概率表示发震的可能程度可作为马氏链预测发震时间的补充；
Using cumulative probability to estimate the possible stage of earthquake occurrence can fulfill the origin time of Markov chain.
用累积概率表示发震的可能程度可作为马氏链预测发震时间的补充；
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