图1地震发生在各组断层上的后验概率分布直方图fig。
Histogram of posterior probability distribution for earthquake to occur on various fault groups.
蒙特卡洛法能根据后验概率分布产生大量的模型，并能用模型的相关似然性质来分析和呈现这些模型。
Monte Carlo method can generate a large collection of models according to the posterior probability distribution and analyses and display the models with relative likelihood of model properties.
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.
更进一步地，因种类分布密度无法从那样的训练集中进行估计，种类的后验概率也无法被估计出来。
Moreover, as class density estimates cannot be derived for such a training set, class posterior probabilities cannot be estimated.
最后，使得粒子的分布更加接近状态的后验概率密度，最大化地实现其滤波性能。
Finally, the particle distribution approaches to the station posterior distribution and the maximum filtering performance is achieved.
最后，使得粒子的分布更加接近状态的后验概率密度，最大化地实现其滤波性能。
Finally, the particle distribution approaches to the station posterior distribution and the maximum filtering performance is achieved.
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