本文提出了类条件概率密度随机变量（特征）空间离散化及类条件概率分布估计方法。
A discrete method for stochastic variable (features) space of class-conditional-probability density and estimation method for class-conditional -probability distribution is proposed.
频谱参数的预测公式由各基频下的条件概率密度函数导出，频谱参数的修改通过正弦模型实现。
The spectral parameters were predicted using the conditional probabilistic distribution function based on the pitch and were modified synchronizingly with the pitch using the sinusoidal model.
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.
而在各高斯分量概率密度互不重叠的条件下，使用动态簇算法(DC)则可快速而精确地估计出混合高斯模型参数。
And if there are no overlaps between each Gaussian component, parameters of Gaussian mixture PDF model can be exact estimated quickly with the dynamic cluster algorithm (DC).
研究了次级电子倍增条件以及次级电子出射角度和出射速度概率密度分布的特点。
The qualification of multipactor and the characteristic of the angle and velocity of secondary electron were study.
研究了次级电子倍增条件以及次级电子出射角度和出射速度概率密度分布的特点。
The qualification of multipactor and the characteristic of the angle and velocity of secondary electron were study.
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