后验概率

posterior probability───[数]后验概率；事后概率

posterior probabilities───后验概率

prior probability───先验概率，事前机率

conditional probability───条件概率；条件几率

empirical probability───实验概率；[数]经验概率

prior probabilities───先验概率

classical probability───古典概率

SD as the initial model to the maximum a posteriori probability method in 150 training samples to identify the best, can reach 90.4 .───以SD为初始模型的最大后验概率方法在150个训练样本时识别效果最好，可以达到90．4％ 。

Bayesian is one kind of method of posteriori probability obtained from priori probability according to new information.───贝叶斯方式是依据新的信息从先验概率得到后验概率的一种方式。

Last, combined with KNN algorithm for multi label classification, the maximum posteriori probability prediction function label unknown function protein is based.───最后，结合多标签分类的k近邻算法，统计k个近邻中蛋白质的功能类数目，基于最大后验概率预测未知功能蛋白质属于的功能标签类。

the general posteriori probability of each acoustic element is worked out and regarded as the scores for the confidence level.───计算每个音素的广义后验概率并将其作为置信度得分。

Probability models of classification, after determining the posteriori probability, can do optimal forecast to given measurement of x.───分类概率模型确定了后验分类概率，就可以对给定的测量x做出最优的预测。

It chooses the number of edges and their locations according to the maximum a posteriori probability (MAP) principle.───它选择的数量及其位置边缘根据最大后验概率（MAP）的原则。

SD as the initial model to the maximum a posteriori probability method in 150 training samples to identify the best, can reach 90. 4 .───以SD为初始模型的最大后验概率方法在150个训练样本时识别效果最好，可以达到90.

the likelihood probability of an acoustic space and the general posteriori probability are calculated after trimming;───计算剪枝后声学空间的似然概率和以及广义后验概率；

Adaptive nonlinear class estimation algorithm using parallel subband HMM maximum a posteriori probability───并行子带HMM最大后验概率自适应非线性类估计算法

Source decoder calculates parameter's posteriori probability based on bit reliability from iterative channel decoder and residual redundancy of source parameter.

The Bayesian estimation is derived through statistical analysis and it is shown to be equivalent to the maximization of the a Posteriori Probability.

The application of a Gaussian Markov Random Fields (GMRF) based Maximum A Posteriori Probability (MAP) estimation for image Gaussian noise filter was presented.