基于变量修正分布的混凝土结构长期变形预测Prediction of long-term deformation for concrete structure based on revised distribution of variables

由于随机因素的影响,混凝土结构长期变形通常表现出很强的离散性,Bayesian理论提供了改善这种离散性的方法。基于Bayesian理论的混凝土结构长期变形预测方法的核心是引入短期变形构造似然函数,通过修正先验概率得到长期变形的后验概率分布。但是对于实际结构而言,在施工之前短期变形及其标准差无法获取,这就使得这种方法在开展时机和实际应用方面存在一定的限制性。为了改善这种限制性,在随机变量修正分布的基础上,结合拉丁超立方抽样技术,采用CEB‐FIP（MC90）模型建立了钢筋混凝土梁长期变形的随机分析模型。采用该模型进行混凝土梁长期变形随机分析,得到基于变量修正分布的混凝土梁长期变形预测结果,并分别与先验预测结果和Bayesian预测结果进行比较。研究结果表明,基于变量修正分布的预测结果与Bayesian预测结果十分接近,比先验预测结果不确定性降低50％左右,与试验结果吻合良好。     

基于贝叶斯网络的信息安全风险评估方法

在系统分析信息安全风险要素及评估过程的基础上，针对评估过程中的不确定性信息难以量化处理的问题，引入贝叶斯网络推理算法，并结合专家知识给出贝叶斯网络下的推理规则条件概率矩阵，从而构建了信息安全风险评估模型．最后以实例分析了基于此模型的风险评估方法．仿真结果表明该方法是一种有效的评估算法，较为准确地了反映了信息安全的风险等级，为信息安全风险评估提供了一种新的思路．     

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules

We investigate the use of different Machine Learning methods to construct models for aqueous solubility. Models are based on about 4000 compounds, including an in-house set of 632 drug discovery molecules of Bayer Schering Pharma. For each method, we also consider an appropriate method to obtain error bars, in order to estimate the domain of applicability (DOA) for each model. Here, we investigate error bars from a Bayesian model (Gaussian Process (GP)), an ensemble based approach (Random Forest), and approaches based on the Mahalanobis distance to training data (for Support Vector Machine and Ridge Regression models). We evaluate all approaches in terms of their prediction accuracy (in cross-validation, and on an external validation set of 536 molecules) and in how far the individual error bars can faithfully represent the actual prediction error.     

Reconstructing Nonparametric Productivity Networks

Network models provide a general representation of inter-connected system dynamics. This ability to connect systems has led to a proliferation of network models for economic productivity analysis, primarily estimated non-parametrically using Data Envelopment Analysis (DEA). While network DEA models can be used to measure system performance, they lack a statistical framework for inference, due in part to the complex structure of network processes. We fill this gap by developing a general framework to infer the network structure in a Bayesian sense, in order to better understand the underlying relationships driving system performance. Our approach draws on recent advances in information science, machine learning and statistical inference from the physics of complex systems to estimate unobserved network linkages. To illustrate, we apply our framework to analyze the production of knowledge, via own and cross-disciplinary research, for a world-country panel of bibliometric data. We find significant interactions between related disciplinary research output, both in terms of quantity and quality. In the context of research productivity, our results on cross-disciplinary linkages could be used to better target research funding across disciplines and institutions. More generally, our framework for inferring the underlying network production technology could be applied to both public and private settings which entail spillovers, including intra- and inter-firm managerial decisions and public agency coordination. This framework also provides a systematic approach to model selection when the underlying network structure is unknown.     

基于学习效应的供应链信息共享研究

供应链中存在着广泛的信息共享,既包括上下游企业间的纵向性共享,也包括同层企业间的横向信息共享.以一个具有学习效应的供应链为研究对象,为研究信息共享对分散型供应链中零售商决策的影响,提出了一个具有横向信息共享的供应链模型.以Cournot博弈为研究手段,求解了零售商的均衡订货决策和信息共享策略.在此模型中,生产商为多个零售商提供类似的产品,每个零售商具有自己独立的终端市场.零售商面对单周期需求,该需求可以在本周期内满足或者在第二阶段延迟交货.由于学习效应的存在,第二阶段生产商的批发价格是第一阶段总订货量的减函数.零售商在观察到自身的需求之前,达成信息共享的协议.研究结果表明,当第一阶段的均衡订货数量低于需求时,零售商间无共享私有信息的动机,该结果和寡头模型信息共享的相关结果相反.除此之外,在一个总体需求稳定的市场中,信息共享的影响随着零售商数量的增加而递减.此结果对企业在不同市场情况下选择信息共享策略具有重要价值.     

RVM有监督特征提取与Seyfert光谱分类

特征提取是光谱自动识别中的一个基本问题,它决定着光谱识别系统的性能和复杂度。目前的天体光谱自动分类研究主要使用的是基于以线性主成分分析(PCA)、小波变换(Wavelet transform)、人工神经网络(ANN)等为代表的非监督特征提取方法,而它们在特征提取时没有考虑到训练数据中的类别信息,并非按照分类能力进行特征选择和降维。文章研究了相关向量机(RVM)有监督特征提取方法及其在Seyfert光谱细分类中的应用。RVM是机器学习领域在近几年提出的一种Bayesian学习方法,它能有效地融合已有的先验知识、对问题的信念、训练数据和相应的类别信息,并按照分类能力提取特征,在理论上具有很大的潜在优势。另外,初步的实验结果表明,基于RVM的有监督特征提取方法在Seyfert光谱细分类中具有较好的性能。     

基于重要度的分级贝叶斯网络诊断模型研究

为了提高复杂系统故障检测和诊断的准确性,从数据的不确定性、诊断的不确定性以及特征参数选择的不确定性三方面展开研究。在贝叶斯网络解决不确定性问题优势的基础上,提出了基于重要度的分级贝叶斯网络模型。以往基于贝叶斯网络的方法无法做到系统地选择参数,建立的模型都是全模型。虽然全模型没有遗漏关键的参数,但包含了很多不必要的参数,这些多余参数不仅会对诊断造成不良影响,导致拖尾效应。仿真结果表明,提出方法可以获得更佳的故障特征,有利于提高故障识别能力,验证了模型的有效性。     

基于贝叶斯理论的全球海温异常对500 hPa 温度场的影响分析

本文利用经验正交函数(EOF) 将海表温度(SST) 距平场进行分解, 得到一组相互正交的模态构成重构空间, 然后在该空间中展开500 hPa温度场, 进一步借助贝叶斯分析方法定义各个模态对温度场的影响指数, 并研究指数随不同海温分布型(模态) 的变化特征. 结果发现SST场在4-6月份对500 hPa温度场的影响较大, 且气候发生转变后, 不同海温分布型对温度场的影响不同.     

基于Contourlet变换的遥感图像去噪新算法

提出了一个新的有效的基于Contourlet变换的遥感图像去噪方法。对有噪图像进行Contourlet分解;对Contourlet变换系数引入一个几何先验模型,结合噪声和有用信号的条件分布进行贝叶斯估计,得到每一系数作为有用信号的后验概率,以之作为修正因子修正小波萎缩因子;对重构图像进行递归循环运算处理。仿真实验结果表明,去噪后图像去除了常见的伪吉布斯现象,峰值信噪比提高了1~2 dB。     

Associations between Longitudinal Gestational Weight Gain and Scalar Infant Birth Weight: A Bayesian Joint Modeling Approach

Despite the importance of maternal gestational weight gain, it is not yet conclusively understood how weight gain during different stages of pregnancy influences health outcomes for either mother or child. We partially attribute this to differences in and the validity of statistical methods for the analysis of longitudinal and scalar outcome data. In this paper, we propose a Bayesian joint regression model that estimates and uses trajectory parameters as predictors of a scalar response. Our model remedies notable issues with traditional linear regression approaches found in the clinical literature. In particular, our methodology accommodates nonprospective designs by correcting for bias in self-reported prestudy measures; truly accommodates sparse longitudinal observations and short-term variation without data aggregation or precomputation; and is more robust to the choice of model changepoints. We demonstrate these advantages through a real-world application to the Alberta Pregnancy Outcomes and Nutrition (APrON) dataset and a comparison to a linear regression approach from the clinical literature. Our methods extend naturally to other maternal and infant outcomes as well as to areas of research that employ similarly structured data.