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prior probability相关的网络例句

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与 prior probability 相关的网络例句 [注:此内容来源于网络,仅供参考]

Then we introduced transition probability kernel for the hidden state process and the confederated process. In the deep research, we found that some similar parameters of state transition probability, observation symbol probability and initial state probability were involved in the transition probability kernel of the confederated process. So we can complete the training of traditional HMM parameters by training the transition probability kernel of the confederated process. To our surprised, we got the consistency of transition probability kernel of the confederated process. Meanwhile, we proved the rationality and reliability of the parameters estimation equations under the classical B-W algorithm.

然后对联合过程的转移概率核展开研究,我们根据过程的取值空间的不同,引入了隐状态空间和联合过程空间上的转移概率核,研究中发现在联合空间上的转移概率核包含了在一般的隐马氏模型的参数估计中的所有类似的三组参数(转移概率,观测概率和初始概率),因此对传统的隐马氏模型的参数训练我们可以通过联合过程的转移概率核的训练来完成,更令我们惊奇的是联合过程的转移概率核还具有一致性;同时对于Baum提出的经典的B-W算法给出的关于模型的参数重估计公式,证明了它的合理性和可靠性,即模型经过重估计后的参数能够满足给定的单观测序列在模型下发生的概率是单调递增的。

Chapter 1 we introduce the risk process with positive and negative risk sums,show the background of the risk process. Chapter 2 we show the integral and differential equation of birth probability,give the Lundberg inequation that the ruin probability satisfies with martingale approach,then discuss then ruin probability of the risk process with two dependent positive and negative risk sums,study how the dependence impacts on the ruin probability. Chapter 3 we compare the concrete examples by numbers,and makes further comparison between the two results coming out of independency and dependency,with the purpose of narrating their respective impacts on the probability of ruin probability.

本文第一章引入含正、负风险和的风险模型,介绍风险过程的实际背景;第二章给出生存概率Φ所满足的积分-微分方程,利用典型鞅方法给出破产概率Ψ满足的Lundberg不等式,并且讨论两个相关正、负风险和模型的破产概率,研究相关性对破产概率的影响;第三章对具体实例给出数值比较,进一步把相关性和独立性两种情形的结果进行比较,说明对破产概率的影响。

Based on probability theory, a method for describing and calculating the collision probability between rendezvous and docking vehicle and debris is presented, and the method involves quantitatively analyzing the collision probability using quasi maximum instantaneous collision probability and total collision probability. The method calculating the indexes is developed, and the input required to perform a calculation includes the respective state vectors, position error covariance matrices and physical sizes of objects involved.(3) Two methods to analyze relative trajectory safety between chaser and target are developed, which are 3-sigma ellipsoid based methodology and collision probability based methodology.

1分析了交会对接各阶段面临的主要轨迹安全威胁,阐述了被动和主动安全策略、目标器的交会对接控制区域等保证交会安全的措施;(2)基于概率思想发展了线性和非线性运动情况下飞行器与空间碎片的碰撞判断方法,提出采用总碰撞概率和拟最大瞬时碰撞概率来综合描述飞行器与空间碎片的碰撞危险程度,推导了通过飞行器和空间碎片各自的状态矢量、位置误差协方差矩阵以及形状尺寸来计算碰撞概率的公式;(3)提出了两种分析追踪器和目标器之间相对轨迹安全的新方法:基于3σ椭球的轨迹安全分析方法和基于碰撞概率的轨迹安全分析方法。

We will demonstrate the specification of reference prior distribution on the random covariance matrix. We will consider the approximate uniform shrinkage prior and approximate Jeffreys' prior. Both are formulated based on the approximated likelihood function. Under generalized linear mixed models, we show that the posterior mode under Jeffrey's prior is asymptotically closer to the REML estimate than the mode under uniform shrinkage prior does.

在广义线性混合模式下,我们证明了在样本数很大时,随机效应之变异数成份的后验众数和REML估计值会近似相等,同时也建立了后验众数和REML估计值的近似关系式,并提出了比较二种后验众数与REML估计值之间远近的准则。

Events,Operation and Relation of Sets, Classical Probability, Geometrical Probability , Statistical Stability of a Frequency, Axioms of Probability, Conditional Probability, Total Probability Theorem, Bayes' Rule,Independent Events,Independent Repeated Trials, One Dimensional Random Variables, Discrete Random Variables, Distribution Function of a Random Variables , Continuous Random Variables, Normal Distribution, Distribution of a Function of a Random Variable, Multidimensional Random Variables, Joint Distribution Function, Marginal Distribution Function,Discrete Two—Dimensional Random Variables,Continuous Two—Dimensional Random Variables, Independent Random Variables, Distribution of Functions of Random Variables,Expectation,Variance, Covariance, Coefficient of Correlation, Bivariate Normal Distribution, Law of Large Numbers, The Central Limit Theorems, Sample and Population ,Chi—Squared, T and F Distributions , Sampling Distributions , Point Estimation , Interval Estimation , Testing Hypotheses , A Test of Significance for Parameters in a Single Sample From a Normally Distributed Population , A Test of Significance for Parameters in Two Sample From Normally Distributed Populations .

本课程的主要内容:概率的概念与运算、随机变量及其分布、随机变量的数字特征与极限定理、数理统计的基本概念、估计和检验的基本方法,随机事件与概率随机事件、事件的关系与运算、几何概率、统计概率等,条件概率、全概率公式、贝叶斯公式、事件的独立性、二项概率公式,随机变量的概念、离散型随机变量、随机变量的分布函数、连续型随机变量、随机变量函数的分布,多维随机变量及其分布函数、边缘分布函数、随机变量的独立性、二维随机变量函数的分布,数学期望、方差、协方差和相关系数、大数定律、中心极限定理,总体与样本, X 2-分布、 t-分布和 F-分布,统计量及抽样分布,假设检验的基本概念、单个正态总体参数的显著性检验、两个正态总体参数的显著性检验。

Firstly,the classification of probability rule is analyzed on the base of classic rough set concepts and extended to the equal relation of set in the indefinite system,namely,the upper and lower approximation space of research set is expressed in the form of conditional probability;then,according to the measure of probability rule,the attributes reduction is carried out and the classification rule is extracted by using the related parameters of condition attributes' impend precision from the angle of conditional probability;Finally,the related simulation test result is given and the result shows the classification rules with probability measures is more rational.

首先在经典粗糙集概念的基础上分析概率规则的分类,并将其推广到不确定系统的集合等价关系中,即用条件概率的形式表示研究集合的上下近似空间;然后根据概率规则的测度从条件概率的角度利用条件属性的逼近精度的相关参数进行属性集的约简进而提取分类规则;最后给出了相关的仿真实验结果,结果表明带有概率测度的分类规则更合理。

So Freedom is in the meditation Prior to attention, Prior to sleep, Prior to death, Prior to the body-mind.

因此自由在先于注意力的冥想中,先于深睡,先于死亡,先于这身-心。

The results showed that, on code evaluation performance, the effect of self-explanation was not significant between groups, but high prior knowledge learners outperformed low prior knowledge learners. On code generation performance, the guiding-question group outperformed the principle- prompt group and the control group. And the high prior knowledge group scored significantly higher than the low prior knowledge group. Finally, on the analysis of attitudes toward self-explanation activities, the guiding-question group showed more positive acceptance than the principle-prompt group.

研究结果发现:(1)在程式评估问题上,自我解释学习活动对学习成效没有显著影响,但高先备知识组则显著优於低先备知识组;(2)在程式设计问题上,问题引导组显著优於其余二组,高先备知识组亦显著优於低先备知识组;(3)在学习态度方面,问题引导组对自我解释学习活动的接纳程度和学习成效的看法上,皆显著高於原则提示组;两组对自我解释学习活动难度的看法则无显著差异。

Monte Carlo is a method that approximately solves mathematic or physical problems by statistical sampling theory. When comes to Bayesian classification, it firstly gets the conditional probability distribution of the unlabelled classes based on the known prior probability. Then, it uses some kind of sampler to get the stochastic data that satisfy the distribution as noted just before one by one. At last, it can obtain the posterior probability distibution of each unlabelled classes by analysing these stochastic data.

蒙特卡罗是一种采用统计抽样理论近似求解数学或物理问题的方法,它在用于解决贝叶斯分类时,首先根据已知的先验概率获得各个类标号未知类的条件概率分布,然后利用某种抽样器,分别得到满足这些条件分布的随机数据,最后统计这些随机数据,就可以得到各个类标号未知类的后验概率分布。

DYS522 sites examine proptosis 5 discrimination power with personal allele,allelomorph to 0.6495,the excluding probability of paternit is;DYS508 sites examine proptosis 6 discrimination power with personal allele, allelomorph to 0.7006,the excluding probability of paternit is;DYS632 sites examine proptosis 3 discrimination power with personal allele,allelomorph to 0.5224,the excluding probability of paternit is;DYS556 sites examine proptosis 6 discrimination power with personal allele,allelomorph to 0.7454,the excluding probability of paternit is.

DYS522基因座共检出5个等位基因9~13,频率分布范围0.0447-0.5075,11频率最高(0.5075),DP值为0.6495;DYS508基因座共检出6个等位基因8~13,等位基因频率分布范围0.0149~0.4030,11频率最高(0.4030),DP值为0.7006;DYS632基因座共检出3个等位基因10~12,等位基因频率分布范围0.0149~0.4926.11频率最高(0.4926),DP值为0.5224;DYS556基因座共检出6个等位基因9~14。等位基因频率分布范围0.0149~0.3433,9频率最高(0.3433),DP值为0.7454。

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