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- a posteriori conditional probability density 后验条件概率密度函数
- conditional probability density function 条件概率密度函数
- A posteriori conditional probability density function 后验条件概率密度函数
- A neural network model based on Gaussian Mixture Models has been devised by Dirk Husmeier in 1997 in order to predict the conditional probability densities. Dirk Husmeier于1997年设计了一个基于高斯混合模型(简称GM模型)的神经网络用来预测概率密度。
- conditions probability density 条件概率密度
- Empirical Likelihood Confidence Interval for a Conditional Probability Density Under m-Dependent Sample m-相依样本下条件密度的经验似然置信区间
- Coverage Accuracy of Confidence Intervals for a Conditional Probability Density Function 条件密度置信区间的覆盖精度
- Since it is the probability density, it must be single-valued. 因为它是几率密度,因此必须是单值的。
- We get a buildup of electronic probability density between the nuclei. 我们得到在两个核间的电子几率密度的堆积。
- conditional probability density 条件概率密度
- Sample Percentage Presentation of Joint Probability Density of Two Random Variables. 二维随机变数联合机率密度之取样百分比。
- The analytic solutions of the probability density of the steady-state responses of Coulomb sliding systems with gradually strong springs to Gaussian white noise are derived. 利用等效线性化方法推导了具有渐硬非线性复位弹簧的滑动系统的高斯白噪声激励稳态随机响应概率分布的解析解。
- If cumulative is TRUE, EXPONDIST returns the cumulative distribution function; if FALSE, it returns the probability density function. 如果cumulative为TRUE,函数EXPONDIST返回累积分布函数;如果cumulative为FALSE,返回概率密度函数。
- In this paper, the method of generating random number with given probability density function is proposed by using vertical density representation. 摘要本文首先介绍基于垂直概率密度表示的,给定密度函数的随机数生成的通用方法;
- The structure of PMN is a four layer feedforward neural networks(FNN), where the Gaussian probability density function is realized as an internal node. PMN网为一个四层前馈网,它构成一个贝叶斯分类器,实现多类分类的贝叶斯判别,把输入的说话人语音数据模型参数通过网络变换为输出的说话人判定。
- Based on the diffusion equation, the transition probability density of stock prices is calculated by means of the Monte-Carlo method. 摘要在扩散方程对股价运行描述的基础上,用蒙特卡罗方法得出未来某一时刻股价转移概率密度的数值解。
- For the accident assessment probability density function can be used to solve the uncertainties of estimation of the source intensities. 应用几率密度函数可解决事故预测时源强估算的不确定性问题。
- The binned kernel density estimators were exploited to estimate the probability density function of background intensity in training sequence. 该模型采用分箱核密度估计算法从训练图像序列中得到背景的密度函数。
- Based on probability density approximation, a novel unsupervised feature ranking approach was proposed and could be applied to feature selection. 摘要依据概率密度逼近提出了一种新的无监督特征排序,应用于特征选择降维。