贝叶斯决策理论与归纳逻辑   总被引：6，自引：0，他引：6  

熊立文 《北京师范大学学报(社会科学版)》2005,(2):108-113

贝叶斯决策理论是主观贝叶斯派归纳理论的重要组成部分。现代归纳逻辑遇到 “归纳接受”的难题,一部分学者用贝叶斯决策理论的思想和方法解决这个问题,后来导致了信念 修正理论的产生。从只考察归纳推理前提与结论之间的逻辑联系到全面地刻画归纳推理的动态 认知过程,现代归纳理论的发展呈现出一种新的面貌,而贝叶斯决策理论是其中承上启下的一 环。对贝叶斯决策理论的检验和修正导致了富有成果的新理论的建立。  相似文献   

Data-driven facial animation based on manifold Bayesian regression   总被引：1，自引：0，他引：1  

WANG Yu-shun ZHUANG Yue-ting WU Fei 《浙江大学学报(A卷英文版)》2006,7(4):556-563

INTRODUCTION Realistic facial animation is highly important in the computer graphics field as it is an essential facility for human-computer interface and virtual reality and is also a difficult task because there are so many non-rigid motions besides rigid motion of heads as expression changes. Instead of modelling all the complicated facial motions, data-driven facial ani- mation just exploits facial motion data captured in real scenes. Most motion capture systems rely upon the placement…  相似文献   

Brief Research Report: Bayesian Versus REML Estimations With Noninformative Priors in Multilevel Single-Case Data     

Eunkyeng Baek  S. Natasha Beretvas  Wim Van den Noortgate  John M. Ferron 《Journal of Experimental Education》2020,88(4):698-710

Abstract

Recently, researchers have used multilevel models for estimating intervention effects in single-case experiments that include replications across participants (e.g., multiple baseline designs) or for combining results across multiple single-case studies. Researchers estimating these multilevel models have primarily relied on restricted maximum likelihood (REML) techniques, but Bayesian approaches have also been suggested. The purpose of this Monte Carlo simulation study was to examine the impact of estimation method (REML versus Bayesian with noninformative priors) on the estimation of treatment effects (relative bias, root mean square error) and on the inferences about those effects (interval coverage) for autocorrelated multiple-baseline data. Simulated conditions varied with regard to the number of participants, series length, and distribution of the variance within and across participants. REML and Bayesian estimation led to estimates of the fixed effects that showed little to no bias but that differentially impacted the inferences about the fixed effects and the estimates of the variances. Implications for applied researchers and methodologists are discussed.  相似文献   

Software Packages for Bayesian Multilevel Modeling     

Yujiao Mai 《Structural equation modeling》2018,25(4):650-658

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.  相似文献   

Mediation Effects In 2-1-1 Multilevel Model: Evaluation Of Alternative Estimation Methods     

Jie Fang  Kit-Tai Hau 《Structural equation modeling》2019,26(4):591-606

We compared six common methods in estimating the 2-1-1 (level-2 independent, level-1 mediator, level-1 dependent) multilevel mediation model with a random slope. They were the Bayesian with informative priors, the Bayesian with non-informative priors, the Monte-Carlo, the distribution of the product, the bias-corrected, and the bias-uncorrected parametric percentile residual bootstrap. The Bayesian method with informative priors was superior in relative mean square error (RMSE), power, interval width, and interval imbalance. The prior variance and prior mean were also varied and examined. Decreasing the prior variance increased the power, reduced RMSE and interval width when the prior mean was the true value, but decreasing the prior variance reduced the power when the prior mean was set incorrectly. The influence of misspecification of prior information of the b coefficient on multilevel mediation analysis was greater than that on coefficient a. An illustrate example with the Bayesian multilevel mediation was provided.  相似文献   

Application of the Bayes’ theory to the failure rate evaluation of electrical apparatus     

JIN Shao-hua  LU Jian-guo  WAN Yan-ping  SUN Shu-guang 《浙江大学学报(A卷英文版)》2007,(3)

The Weibull distribution has been widely used in reliability fields. A mixed Weibull distribution represents a popu- lation that consists of several Weibull subpopulations. In this paper, a new approach which combines the least-squares method with Bayes’ theorem, takes advantage of the parameter estimation for single Weibull distribution is developed to estimate the pa- rameters of each subpopulation. The estimates given by this paper also satisfy the maximum likelihood equation. The estimates of the failure rate of the mixed Weibull population are given. An actual test data is computed by using the proposed method. The Kolmogorov-Smirnov goodness-of-fit test turns out that the proposed method yields more accurate result.  相似文献   

Measurement Invariance Testing with Many Groups: A Comparison of Five Approaches     

Eun Sook Kim  Chunhua Cao  Yan Wang  Diep T. Nguyen 《Structural equation modeling》2017,24(4):524-544

With the increasing use of international survey data especially in cross-cultural and multinational studies, establishing measurement invariance (MI) across a large number of groups in a study is essential. Testing MI over many groups is methodologically challenging, however. We identified 5 methods for MI testing across many groups (multiple group confirmatory factor analysis, multilevel confirmatory factor analysis, multilevel factor mixture modeling, Bayesian approximate MI testing, and alignment optimization) and explicated the similarities and differences of these approaches in terms of their conceptual models and statistical procedures. A Monte Carlo study was conducted to investigate the efficacy of the 5 methods in detecting measurement noninvariance across many groups using various fit criteria. Generally, the 5 methods showed reasonable performance in identifying the level of invariance if an appropriate fit criterion was used (e.g., Bayesian information criteron with multilevel factor mixture modeling). Finally, general guidelines in selecting an appropriate method are provided.  相似文献   

Accounting for Model Uncertainty in the Prediction of University Graduation Rates     

Cullen F. Goenner  Sean M. Snaith 《Research in higher education》2004,45(1):25-41

Empirical analysis requires researchers to choose which variables to use as controls in their models. Theory should dictate this choice, yet often in social science there are several theories that may suggest the inclusion or exclusion of certain variables as controls. The result of this is that researchers may use different variables in their models and come to disparate conclusions with respect to predicted effects and their statistical significance. In such cases one is uncertain of which particular set of regressors forms the model that represents the data. The approach used below accounts for uncertainty in variable selection by using Bayesian model averaging (BMA). Accounting for uncertainty, we demonstrate that BMA provides better out-of-sample prediction for university graduation rates than results based on alternative variable selection methods.  相似文献   

基于贝叶斯网络的空间受限项目风险管理研究     

柴国荣  黄龙沣  宗胜亮 《科技管理研究》2017,(14)

空间受限项目的事故风险源多,且易诱发连锁反应。如何快速识别空间受限项目的风险源,并有效评估各个诱发节点的风险大小,对空间受限项目的风险管理具有重要意义。识别空间受限项目风险源并构造贝叶斯网络,通过贝叶斯网络推理的正向预测、反向诊断,获得各个风险源节点的后验概率,并计算风险源概率与事故发生的互信息,同时引入风险可控值概念及指标量化,构建基于贝叶斯网络的二维四区风险管理模型,提出空间受限项目风险源管理策略,最后通过一个实例对研究结果进行说明。  相似文献   

高速公路事件实时响应层次决策支持系统(英文)     

杨顺新  倪富健  陈飞 《东南大学学报》2009,25(4):536-540

应用人工智能技术产生高速公路事件响应预案,提出了基于规则推理、案例推理和贝叶斯网络推理3种方法的事件响应框架. 首先, 建立了基于规则推理的高速公路事件管理系统(RK-IMS), 并应用于宁连高速公路北段事件管理过程中. 然后, 通过分析RK-IMS系统2年的运营数据, 确定了事件案例的结构表示与事件响应的贝叶斯网络结构. 最后, 应用k近邻算法计算相似性案例, 并研究了基于该算法的预案产生和控制策略. 利用2006年RK-IMS事件管理系统的实际数据对模型进行了验证. 对比分析结果表明, 该方法是有效可信的.  相似文献