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.
本文讨论非线性随机离散系统状态估值问题的测度变换，推得系统的状态对于观测的条件分布律。
We obtain an explicit formulae for the conditional distribution laws of the state of the dynamical system with respect to the observed data.
水文站是一个离散分布的对象，与地理位置、地质条件密切相关。
Hydrologic station is a distributive organization and relates to geography location and geological condition.
分析了离散时间线性系统模型参数估计误差的收敛性和收敛速度，对参数估计误差服从渐近正态分布的一些条件进行了讨论。
The convergent property and convergent rate of parameter estimation error are analyzed . Some sufficient conditions are given to guarantee the asymptotic normality of parameter estimation error.
依据粗集理论研究离散化数据的特点，考虑类分布信息，采用信息熵理论进行连续条件属性的离散化。
Data discrimination is the character of RS, considering distributed information of class, and continual condition attributes are described according to information entropy theory.
研究结果表明：在低信噪比条件下，激光二极管雷达测距数据的离散分布将明显偏离正态分布；
The results show that:in the low SNR conditions, the discrete distribution of the laser diode radar ranging data will significantly deviate from the normal distribution;
研究结果表明：在低信噪比条件下，激光二极管雷达测距数据的离散分布将明显偏离正态分布；
The results show that:in the low SNR conditions, the discrete distribution of the laser diode radar ranging data will significantly deviate from the normal distribution;
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