先验概率与后验概率有不可分割的联系，后验概率的计算要以先验概率为基础。

The posterior probability is computed from the prior and the likelihood function via Bayes' theorem.

先验概率值大于0.8的标在了分支节点处。

Values of Bayesian posterior probability greater than 0.8 are shown at the nodes.

在最大似然法的程序实现中，采用了最小距离法提供先验概率。

Minimum Distance algorithm is adopted to supply prior probabilities of each type required in Maximum Likelihood algorithm.

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。

Bayesian is one kind of method of posteriori probability obtained from priori probability according to new information.

在目标识别级重点讨论了基于D - S证据理论的目标识别融合，通过性能分析可知该算法具有不需要先验概率和条件概率密度等优点。

In object identification level object identification fusion based on D-S proof theory was discussed, performance analyzing is found that the arithmetic did not need probability distribution.