基于贝叶斯网络的数据挖掘技术

从海量数据中挖掘有用的信息为高层的决策支持和分析预测服务。已成为网络时代人们对信息系统提出的新的需求。但我们发现数据处理和数据的提炼技术是匮乏的。起源于贝叶斯统计学的贝叶斯网络以其独特的不确定性知识表达形式、丰富的概率表达能力、综合先验知识的增量学习方法等特性表示了客体的概率分布和因果联系，成为当前数据挖掘众多方法中最为引人注目的焦点之一。本文首先对贝叶斯网络、贝叶斯网络推理和贝叶斯网络学习进行综合性的阐述。然后讨论其在数据挖掘中的应用和优势。     

贝叶斯网络研究综述

贝叶斯网络将概率理论和图论相结合,为解决不确定性问题提供了一种自然而直观的方法．近年来,贝叶斯网络已成为国内外智能数据处理的研究热点之一,被广泛应用于专家系统、决策支持、模式识别,机器学习和数据挖掘等领域．综述了贝叶斯网络的典型推理和学习算法,并对其进一步的研究方向进行了展望．     

基于贝叶斯网络的统计推断与问题求解

贝叶斯网络近年成为数据采掘引人注目的研究方向。本文介绍贝叶斯网络的结构和建造步骤 ,并着重讨论基于贝叶斯网络、综合先验信息和样本数据进行统计推断和问题求解的基本思想。与数据采掘的其他方法相比 ,贝叶斯网络统计推断的优点是可以综合先验信息和样本信息 ,并且在样本难得或具有不完整数据集时亦能使用 ,从而将使贝叶斯网络在数据采掘中成为一个有力的工具。     

朴素贝叶斯分类算法在数据预测中的应用

朴素贝叶斯分类方法是数据库分类知识挖掘领域的一项基本技术,并具有广泛的应用.使用贝叶斯分类算法实现了对经典数据集Iris的分类.实践表明,朴素贝叶斯分类是一种有效的数据挖掘分类算法.     

基于动态贝叶斯网络的障碍物辨识研究

依据对样本的统计学习和对事物的先验知识,在数据缺失或没有样本数据的情况下依然可以建立有效的分类器。从图像中提取特征,筛选出所需特征,构建贝叶斯网络模型,并计算各节点的条件概率。将所需数据传入建好的网络系统中,通过一系列推理判断得到所需答案。实验结果表明,利用动态贝叶斯网络建立的障碍物辨识系统,能有效实现人和车辆等障碍物的辨识。     

基于贝叶斯网络的学习评估模型及其在E-learning系统的应用

基于贝叶斯网络理论,把领域知识按知识项进行划分,然后在这些知识项之间建立组合依赖关系以确定贝叶斯网络的因果推理关系,最后确定变量的概率参数,建立了基于知识关系的覆盖型贝叶斯网络学习评估模型。并实现了基于贝叶斯网络学习评估模型的E-learning原型系统,系统能够对学生的知识掌握水平进行评估,并根据学生的评估结果,为学生提供个性化的导学建议。     

完备数据集上贝叶斯网络结构学习研究

贝叶斯网络是用来描述不确定变量之间潜在依赖关系的图形模型.从完备数据集上学习贝叶斯网络是一个研究热点,因此分析完备数据集上构建贝叶斯网的常见理论方法非常必要.     

贝叶斯网络在数据挖掘中的应用

数据挖掘是一种新型的数据分析技术。介绍数据挖掘关键技术及挖掘过程,探讨当前数据挖掘所面临的问题,分析将贝叶斯网络应用于数据挖掘的优势。     

朴素贝叶斯基于学生信息库的数据挖掘研究

对数据挖掘技术在研究生信息库中的应用进行了初步分析探讨,目的是从海量的学生数据库中提取人们感兴趣的数据信息,并创建数据挖掘模型。运用朴素贝叶斯分类的方法,对所给数据进行分类和预测,并指出了其技术难点及构建算法,最后,通过一个实例给出了该算法对于预测数据进行分类的详细过程。     

基于BKT模型的网络教学跟踪评价研究北大核心CSSCI

当前网络教学中存在教学过程评价难以量化、课程教学进度安排缺少数据支持等问题。随着大数据技术的发展,网络学习行为分析已经取得较大进展,但学习内容的跟踪与评价还比较缺乏。将知识跟踪嵌入网络课程,及时跟踪学生知识掌握情况,将有助于教师发现学生学习问题,调整教学策略;同时也可引导学生将学习的关注点聚焦在知识内容的理解上,而不是分数上。贝叶斯知识跟踪(BKT)模型是一种以知识点为核心构建学生知识模型的方法,具有简捷、预测准确、易于解释的特点。基于BKT公式改进的网络教学跟踪评价模型,可用以课时估算和学习成绩预测。实证分析数据显示,该模型的预测准确率和精确度较高。在实际应用中,BKT知识跟踪功能可单独开发应用,也可与教学平台集成使用,亦可支持线下教学。     

Bayesian Versus Maximum Likelihood Estimation of Multitrait–Multimethod Confirmatory Factor Models

This article compares maximum likelihood and Bayesian estimation of the correlated trait–correlated method (CT–CM) confirmatory factor model for multitrait–multimethod (MTMM) data. In particular, Bayesian estimation with minimally informative prior distributions—that is, prior distributions that prescribe equal probability across the known mathematical range of a parameter—are investigated as a source of information to aid convergence. Results from a simulation study indicate that Bayesian estimation with minimally informative priors produces admissible solutions more often maximum likelihood estimation (100.00% for Bayesian estimation, 49.82% for maximum likelihood). Extra convergence does not come at the cost of parameter accuracy; Bayesian parameter estimates showed comparable bias and better efficiency compared to maximum likelihood estimates. The results are echoed via 2 empirical examples. Hence, Bayesian estimation with minimally informative priors outperforms enables admissible solutions of the CT–CM model for MTMM data.     

Why do we need to employ Bayesian statistics and how can we employ it in studies of moral education?: With practical guidelines to use JASP for educators and researchers

In this article, we discuss the benefits of Bayesian statistics and how to utilize them in studies of moral education. To demonstrate concrete examples of the applications of Bayesian statistics to studies of moral education, we reanalyzed two data sets previously collected: one small data set collected from a moral educational intervention experiment, and one big data set from a large-scale Defining Issues Test-2 survey (DIT). The results suggest that Bayesian analysis of data sets collected from moral educational studies can provide additional useful statistical information, particularly that associated with the strength of evidence supporting alternative hypotheses, which has not been provided by the classical frequentist approach focusing on P-values. Finally, we introduce several practical guidelines pertaining to how to utilize Bayesian statistics, including the utilization of newly developed free statistical software, Jeffrey’s Amazing Statistics Program (JASP), and thresholding based on Bayes Factors (BF), to scholars in the field of moral education.     

基于贝叶斯公式的决策研究

贝叶斯公式可在客观调查的基础上修正先验概率,为决策者提供更为可靠的信息。列举了贝叶斯公式在经济决策中的应用例子,同时也用贝叶斯公式对生活中信用及舆论方面的某些现象作了诠释。     

基于粗集与贝叶斯分类的电信网络质量预测

在小样本的情况下,仅利用数理统计方法进行预测是不太适合的.在此先应用粗糙集的相关知识来确定影响电信网络质量的主要因素.然后运用贝叶斯理论将统计推断建立在后验分布的基础上,并给出了小样本试验数据的电信网络质量的预测.     

A Tutorial in Bayesian Potential Outcomes Mediation Analysis

Statistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables. Causal interpretation of mediation analyses is challenging because randomization of subjects to levels of the independent variable does not rule out the possibility of unmeasured confounders of the mediator to outcome relation. Furthermore, commonly used frequentist methods for mediation analysis compute the probability of the data given the null hypothesis, which is not the probability of a hypothesis given the data as in Bayesian analysis. Under certain assumptions, applying the potential outcomes framework to mediation analysis allows for the computation of causal effects, and statistical mediation in the Bayesian framework gives indirect effects probabilistic interpretations. This tutorial combines causal inference and Bayesian methods for mediation analysis so the indirect and direct effects have both causal and probabilistic interpretations. Steps in Bayesian causal mediation analysis are shown in the application to an empirical example.     

基于贝叶斯网络的FMEA RPN分析研究

本文对FMEA失效模式与影响分析工具的实质问题进行了探讨,利用贝叶斯网络对FMEA PRN进行了定量分析,阐述了根据故障因子后验概率推断其频度O的等级的理论方法,以改善目前方法对PRN定量分析的不足。结合具体案例进行了贝叶斯网络建模和故障因子后验概率定量计算的过程分析,以体现解决这些实质问题的认知关系。本文研究丰富了在新产品开发和项目管理中广泛使用的FMEA工具的内涵,具有一定的推广价值。     

贝叶斯框架下的自适应质量变量预测模型

针对工业生产过程中的时变性问题,提出贝叶斯网络框架下的自适应质量变量预测建模方法.采用改进的即时学习策略,将数据库分成若干局部数据子集,快速选择与待测样本相似度较高的一组数据作为训练样本,再利用主成分分析对训练样本过程变量进行特征提取,借此作为网络模型输入变量.利用基于改进Figueiredo-Jain算法的EM算法估...     

Software Packages for Bayesian Multilevel Modeling

Multilevel modeling is a statistical approach to analyze hierarchical data that consist of individual observations nested within clusters. Bayesian method is a well-known, sometimes better, alternative of Maximum likelihood method for fitting multilevel models. Lack of user friendly and computationally efficient software packages or programs was a main obstacle in applying Bayesian multilevel modeling. In recent years, the development of software packages for multilevel modeling with improved Bayesian algorithms and faster speed has been growing. This article aims to update the knowledge of software packages for Bayesian multilevel modeling and therefore to promote the use of these packages. Three categories of software packages capable of Bayesian multilevel modeling including brms, MCMCglmm, glmmBUGS, Bambi, R2BayesX, BayesReg, R2MLwiN and others are introduced and compared in terms of computational efficiency, modeling capability and flexibility, as well as user-friendliness. Recommendations to practical users and suggestions for future development are also discussed.     

The Impact of Moderate Priors For Bayesian Estimation and Testing of Item Factor Analysis Models When Maximum Likelihood is Unsuitable

In psychological research, available data are often insufficient to estimate item factor analysis (IFA) models using traditional estimation methods, such as maximum likelihood (ML) or limited information estimators. Bayesian estimation with common-sense, moderately informative priors can greatly improve efficiency of parameter estimates and stabilize estimation. There are a variety of methods available to evaluate model fit in a Bayesian framework; however, past work investigating Bayesian model fit assessment for IFA models has assumed flat priors, which have no advantage over ML in limited data settings. In this paper, we evaluated the impact of moderately informative priors on ability to detect model misfit for several candidate indices: posterior predictive checks based on the observed score distribution, leave-one-out cross-validation, and widely available information criterion (WAIC). We found that although Bayesian estimation with moderately informative priors is an excellent aid for estimating challenging IFA models, methods for testing model fit in these circumstances are inadequate.