双向分类随机效应模型中方差分量经验Bayes估计的收敛速度(英文)

本文在加权平方损失下导出了平衡的双向分类随机效应模型中方差分量的Bayes估计,并利用非参数方法构造了方差分量的经验Bayes(EB)估计.在适当的条件下证明了EB估计的收敛速度.最后,给出一个满足主要结果的例子.     

对称损失函数下定时截尾情形几何分布参数的Bayes估计

首先给出了在对称损失函数下定时截尾情形下几何分布参数的Bayes估计的一般形式.然后在给出先验分布的条件下,给出了Bayes估计的精确形式.最后证明了此Bayes估计的可容许性.     

回归系数的线性估计在平衡损失下的可容许性

针对多元正态线性模型,根据Zellner提出的平衡损失函数,对其进行变形,定义了更一般的平衡损失函数。研究了在该损失下,多元正态线性模型回归系数矩阵的线性估计在一切估计类中的可容许性,并给出了充要条件。该方法同样适用于线性估计类中的可容许性以及其他的可容许性的研究。     

机器学习在个性化网络课程学习中的应用分析

通过机器学习的介绍,说明其在适应性教学系统中的应用。对机器学习系统应用的基础教育教学理论的分析,指出适应性教学是发展方向。对机器学习在个性化网络课程学习中的三种应用方式进行了分析。     

基于贝叶斯的入侵检测

入侵检测技术在网络安全领域的应用越来越重要,它是网络安全防护的重要组成部分。提出将贝叶斯原理应用于入侵检测,把AI领域中的概念引入入侵检测,建立入侵检测的规划识别模型,尝试预测攻击者的下一步行为或攻击意图,从而起到提前预警的作用。用一实例说明了贝叶斯原理在入侵检测领域内的一些应用,提出了一个基于改进贝叶斯算法的新模型。该模型提高了入侵检测系统的完备性和准确性,能有效保障信息系统的安全。     

电视机失效数及失效率的Bayes方案

探讨了电视机失效率鉴定试验的一种Bayes方法。这种方法综合了电视机研制过程中的先验信息，在确保电视机质量的前提下，与传统的鉴定检验方案相比，会大大节省检验时间。     

基于贝叶斯文本分类的高校设备批量集中采购

针对高校设备批量集中采购适用范围狭窄的问题,可尝试新的思路:将批量集中采购的方式由按照采购品目进行集中,改为按照满足招标条件进行集中。由此可以用贝叶斯分类算法将批量集中采购问题转变为对设备分类问题,从而利用计算机及网络的优势,实现跨部门、跨地区的设备汇总。实验结果表明,改进的分类器可以在高校设备采购中进行有效的初步分类及汇总,从而扩大批量集中采购在高校的适用范围。     

Advanced Nonlinear Latent Variable Modeling: Distribution Analytic LMS and QML Estimators of Interaction and Quadratic Effects

Interaction and quadratic effects in latent variable models have to date only rarely been tested in practice. Traditional product indicator approaches need to create product indicators (e.g., x ₁ ², x ₁ x ₄) to serve as indicators of each nonlinear latent construct. These approaches require the use of complex nonlinear constraints and additional model specifications and do not directly address the nonnormal distribution of the product terms. In contrast, recently developed, easy-to-use distribution analytic approaches do not use product indicators, but rather directly model the nonlinear multivariate distribution of the measured indicators. This article outlines the theoretical properties of the distribution analytic Latent Moderated Structural Equations (LMS; Klein & Moosbrugger, 2000) and Quasi-Maximum Likelihood (QML; Klein & Muthén, 2007) estimators. It compares the properties of LMS and QML to those of the product indicator approaches. A small simulation study compares the two approaches and illustrates the advantages of the distribution analytic approaches as multicollinearity increases, particularly in complex models with multiple nonlinear terms. An empirical example from the field of work stress applies LMS and QML to a model with an interaction and 2 quadratic effects. Example syntax for the analyses with both approaches is provided.     

Walking cadence required to elicit criterion moderate-intensity physical activity is moderated by fitness status

ABSTRACT

The aims of this study were to estimate the walking cadence required to elicit a VO₂reserve (VO₂R) of 40% and determine if fitness status moderates the relationship between walking cadence and %VO₂R. Twenty participants (10 male, mean(s) age 32(10) years; VO₂max 45(10) mL·kg^?1·min^?1) completed resting and maximal oxygen consumption tests prior to 7 x 5-min bouts of treadmill walking at increasing speed while wearing an Apple Watch and measuring oxygen consumption continuously. The 7 x 5-min exercise bouts were performed at speeds between 3 and 6 km·h^?1 with 5-min seated rest following each bout. Walking cadence measured at each treadmill speed was recorded using the Apple Watch “Activity” app. Using Bayesian regression, we predict that participants need a walking cadence of 138 to 140 steps·min^?1 to achieve a VO₂R of 40%. However, these values are moderated by fitness status such that those with lower fitness can achieve 40% VO₂R at a slower walking cadence. The results suggest that those with moderate fitness need to walk at ~40% higher than the currently recommended walking cadence (100 steps·min^?1) to elicit moderate-intensity physical activity. However, walking cadence required to achieve moderate-intensity physical activity is moderated by fitness status.     

正态混合模型的Bayes分析

正态混合分布模型是数据概率建模的一种灵活和高效的工具,只要项数足够大,正态混合密度可以任意逼近一个光滑密度。正态混合模型还可以用来对那些不能用标准的参数分布族来拟和的总体进行密度估计或近似。但是在正态混合模型的研究中,存在着两个难题:一是怎样估计混合模型的参数;二是混合成分的个数k的确定。通过对模型参数用Gibbs抽样法来进行抽样,构造生死Markov链(BDMCMC),设计算法确定混合成分的个数k和混合模型的参数。