条件概率

利用几何概型得出均匀分布的边缘密度函数和条件概率密度函数的直观求法。

By the geometric probability model , the intuitionistic method is provided for the marginal density function and conditional probability density function .

对现有堤防工程的失效条件概率进行了定性分析，并提出了其系统可靠度的点值计算方法；

Its conditional probability of failure is researched by qualitative method , and a point value method for the system reliability of existing dikes is presented also .

B值随机变量正则条件概率分布的存在性

Existence of B-valued random variable with regular conditional probability distribution

基于EM算法的因子分析中隐变量的条件概率密度函数

Conditional PDF of Hidden Variable for Factor Analysis Based on EM Algorithm

2）把概率空间（B，F~，P~）中的P~定义为条件概率的疏忽之处。

Some neglected points when P ~ in probability Space ( B , F ~ , P ~ ) is defined as Conditional Probability .

条件概率与刚性近似下高分子链非格子模型的MonteCarlo模拟

A Non-Lattice Model with Conditional Probability for Monte Carlo Simulation of Conformational Statistics of Polyethylene Chain

分组样本下先验BN模型及条件概率的学习算法

Prior BN Model for Group Samples and Conditional Probabilities Learning Algorithm

侧连标记基因型下QTL基因型的条件概率

Conditional Probabilities of QTL Genotypes Under Flanking Marker Genotypes

在概率聚类算法中，类条件概率密度函数定义为样本X到该类聚类中心之间距离的倒数。

In the algorithm , the class conditional probability density function was defined as the reciprocal of the distance between sample X and the cluster center .

HMM是产生式模型的典型代表，ME和CRF属于条件概率模型。

HMM is a typical generative model . ME and CRF both belong to conditional model .

平稳过程的条件概率密度非参数估计的L1－模强相合性

Almost Sure L1 - Norm Convergence of Nonparametric Estimation of Conditional Probability Densities of Stationary processes

给出F事件的条件概率定义和性质，并在基本空间普通划分下给出F事件的全概率公式。

The definition and quality of conditional probability of fuzzy events were given . The total probability formula of Fuzzy events in general division of sample space was also given .

D-S证据理论不需要先验知识和条件概率，可以对相互重叠、非互不相容的多源信息进行融合，是行之有效地数据融合方法。

D-S theory is an effective data fusion method , which could integrate over-lapped related multi-source information without prior knowledge and conditions .

运用条件概率和并集事件的概率展开公式建立了k/n（G）表决系统可靠性分析的通用模型，并给出了具体的计算步骤。

Using the expansion formula of conditional probability and combined event probability , a general model of safety analysis for k / n ( G ) voting system is established and specific operating steps are suggested .

4）通过给每条模糊if-then规则（例如A→B）赋予一个条件概率（p（BA））的方法，提出了带概率因子的加乘型模糊神经网络。

Based on assigning a conditional probability factor ( p ( B | A )) to each if-then fuzzy rule ( e.g. A-B ), Additive-Multiplicative Fuzzy Neural Networks with probability factor has been presented .

如果Rich不保留竖线也挺好，这样我们可以将它作为我们核心DSL的一部分，用来作为条件概率记号。

It would also be nice if Rich doesn 't reserve the vertical bar for anything so we can keep it as part of our core DSL for conditional probability notation .

基于Markov链，条件概率等数学方法，利用多模型、多控制器和平滑切换策略，给出了系统稳定及能量消耗最小的充分条件，所设计的控制器简单、易行。

By several mathematic methods such as Markov chain and stationary transition probabilities , the sufficient condition for system stability with minimal cost is derived by sliding switch between models and controllers .

提出一种基于一类支持向量机（one-ClassSVM）的贝叶斯分类算法，该算法用一类SVM对类条件概率密度进行估计以构造贝叶斯分类器。

A Bayesian classification algorithm based on one-class SVM is presented . It constructs the Bayesian classifier using the classes ' conditional density estimated by one-class SVM .

仿真结果是由用条件概率描述的信道得到的。由于卷积turbo信道的构成是线性的，而且物理信道也有同样的假定。

The results are obtained for channels described by the conditional probability that the constitute turbo convolutional codes are linear . Furthermore , the assumptions about the physical channel hold .

证明采用高斯核的一类SVM，其解可以归一化为密度函数，并把该密度函数看作类条件概率密度的平滑估计，构造贝叶斯分类器。

It is proven that the solution of one-class SVM using the Gaussian kernel can be normalized as an estimate of probability density , and can be used to obtain the Bayesian classifier .

用条件概率方法研究桩基质量随机抽样不合格桩数r的概率分布p（n，N）（r）和数字特征的计算公式。

In this paper , the conditional probability method is used to derive the formulae of probability distribution and statistic expectation of the number of defective piles in testing the ovens guality of a pile foundation by random sampling without repetition .

超有限Loeb空间上的条件概率

Conditional probability in hyperfinite Loeb spaces

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 .

对每一类建立一个径向基函数（RBF）网络，以相应类的边界向量作为中心，通过训练，最终以RBF网络来估计样本的类条件概率密度。

The learning algorithm constructed a radial basis function network with the boundary vectors as the network centers to approximate the class-conditional probability density function of each class of the objects in the training data set .

条件概率（P），模糊条件概率（FP）和列联比（O）构成模糊矩阵R，交叉合成9种预测方法。

The Fuzzy matrixes R were constituted of condition probability ( P ), Fuzzy condition probability ( FP ) and contingency rate ( 0 ) . Nine kinds of the forecasting method were alternatively made up of the Fuzzy vectors and matrixes .

针对现有基于Search的图像标注中存在的不足，如相关图像集合的精度低、用户负担重等，本文尝试通过有效融合伪相关反馈机制，建立伪相关条件概率标注模型。

To overcome the difficulties in search based image annotation , e.g. lower accuracy of relevant images , more burdens on human , the dissertation attempts to integrate the scheme of pseudo relevance feedback into the task of AIA and create the pseudo relevance probability model of automatic image annotation .

其次，基于条件概率分布和降趋脉动分析（DFA）方法，得出两个市场的重现间隔序列具有较强的短期记忆性和长期记忆性，这种特性源于原始收益率本身。

Secondly , we discover short-memory and long-memory effect exist in the recurrence interval series based on conditional probability distribution and detrended fluctuation analysis , which are derived from original return series .

对模拟数据集和真实遥感图像数据集进行实验，验证了本方法的有效性和鲁棒性。（2）提出了一种基于图像最优分割和空间关联条件概率融合的多时相SAR图像变化检测方法。

Experiments carried out on the simulated and the real multi-temporal remote sensing images show that the proposed approach is efficacious . ( 2 ) A new change detection method based on image optimalizing segmentation and spatial conditional probability fusion in multi-temporal SAR image is presented .

传统的因果规则挖掘算法仅能在简单变量间挖掘因果规则，本文给出了一个能够在多值变量X与Y间挖掘形如X→Y的因果规则，且有一条件概率矩阵MY｜X。

Traditional causal mining techniques are developed for mining causal rules among simple variables . This paper proposes a new approach to identifying causality between a pair of muti-value variables X and Y that is represented in the form X → Y with a conditional probability matrix M Y | X.

此外，讨论了在数据完整充足情况下，利用大样本数据统计方法来确定节点的条件概率，和在数据缺失情况下利用EM算法来获取节点的条件概率的方法。

In addition to this , the paper also discusses the integrity of data in sufficient circumstances which will use large samples statistical methods to determine conditional probability and circumstances in missing data which will use EM algorithm to obtain node conditional probability .