线性指数分布参数的Bayes估计

对给定容量为n的线性指数分布样本X1,X2,…,Xn,在Linex损失函数下,利用共轭先验分布讨论线性指数分布参数θ的Bayes估计,多层Bayes估计,E-Bayes估计和极大似然估计.     

熵损失函数下Poisson分布参数的Bayes估计

用类似于Bayes估计的方法,研究了在熵损失函数下Poisson分布可靠度的估计.给出了在熵损失函数下Poisson分布可靠度的Bayes估计及其可容许性,并给出了Bayes置信下限以及多层Bayes估计的表达式.     

数据挖掘技术中"One-R"算法的改进

以我们在现实生活当中处理数据库的经验来看，为了从数据库中使用限制性的信息抽取方法得到有用的数据和信息，我们有必要使用“特点子集选择”的方法(Feature Subset Selection)。而这种方法需要从多个相关的属性当中进行多次的试验从而找到某种特定的子集。在这种情况下，如何能够找到快速的、准确的同时又是简便的选择算法变得越来越关键。这篇文章对由Dr．Robert Ho1tes提出的“One-R”算法进行了适当的改进，通过“Chi”氏独立性检验和拜亚(Bayes Decision Theory)公式推导出的新方法，在试验的基础上表明，这种新的算法在某种程度上比原来的“One-R”算法要准确。     

线性模型中相关附加信息全部损失的条件(英文)

通过讨论回归系数的混合估计和Bayes估计的相关附加信息全部损失的条件,本文给出了线性模型中选择相关附加样本应遵守的一个原则,并研究了两个应用问题。     

基于本体和加权朴素贝叶斯的网络舆情主题分类

及时准确地对舆情信息进行主题分类,不仅能实时了解舆情动态变化,还能为预判舆情发展趋势、舆论引导建立基础。本文提出一种基于本体和加权朴素贝叶斯的网络舆情主题分类方法,通过使用本体将领域知识和领域文本特征融入分类过程中。将该方法应用到动物卫生领域舆情主题分类中,分类结果精确度为0.9402,Marco_F1达到0.9339。通过与朴素贝叶斯（NB）和THUCTC两种方法的对比实验,证明本文提出的基于本体和加权朴素贝叶斯的分类方法有效且具有可行性,但是领域本体的概念、关系的完备程度会影响分类的效率。     

基于概率论的风险型决策及应用

运用概率论处理运筹学中的风险型决策问题,得到一些具有实际意义的结果：运用状态概率和方案的期望值确立风险型决策的指导原则;运用树型图直观表示风险型决策方案的形成过程;以转移概率为特征值对风险型决策方案进行稳定性分析;运用Bayes概率计算公式对决策方案进行前、后验分析和调整．     

逐步Ⅱ型寿命下逆Rayleigh分布参数的估计

在逐步Ⅱ型截尾样本下讨论了逆Rayleigh分布参数的估计问题,给出了参数的最大似然估计,并在三种不同的损失函数下给出了参数的Bayes估计和区间估计.最后给出了数值模拟的例子。     

对全概率和贝叶斯公式寻找完备事件组的两个方法

对全概率公式和贝叶斯公式,探讨了寻找完备事件组的两个常用方法.     

When Can We Improve Subscores by Making Them Shorter?: The Case Against Subscores with Overlapping Items

Subscores can be of diagnostic value for tests that cover multiple underlying traits. Some items require knowledge or ability that spans more than a single trait. It is thus natural for such items to be included on more than a single subscore. Subscores only have value if they are reliable enough to justify conclusions drawn from them and if they contain information about the examinee that is distinct from what is in the total test score. In this study we show, for a broad range of conditions of item overlap on subscores, that the value of the subscore is always improved through the removal of such items.     

Digital Module 11: Bayesian Psychometric Modeling https://ncme.elevate.commpartners.com

In this digital ITEMS module, Dr. Roy Levy describes Bayesian approaches to psychometric modeling. He discusses how Bayesian inference is a mechanism for reasoning in a probability-modeling framework and is well-suited to core problems in educational measurement: reasoning from student performances on an assessment to make inferences about their capabilities more broadly conceived, as well as fitting models to characterize the psychometric properties of tasks. The approach is first developed in the context of estimating a mean and variance of a normal distribution before turning to the context of unidimensional item response theory (IRT) models for dichotomously scored data. Dr. Levy illustrates the process of fitting Bayesian models using the JAGS software facilitated through the R statistical environment. The module is designed to be relevant for students, researchers, and data scientists in various disciplines such as education, psychology, sociology, political science, business, health, and other social sciences. It contains audio-narrated slides, diagnostic quiz questions, and data-based activities with video solutions as well as curated resources and a glossary.