贝叶斯网结构学习的研究现状及发展趋势

目前,在结构已知情况下,贝叶斯网的参数学习算法及数据完备时的贝叶斯网的结构学习算法比较成熟,但是从不完全数据中学习贝叶斯网结构比较困难;文章简要介绍前者,重点分析了在不完备数据条件下结构学习的难点,对现有的学习算法进行了深入的研究和比较,对该领域的研究趋势进行了展望。     

网络环境下研究生信息检索能力调查研究

研究生作为学历层次较高的人群,对信息检索的需求也更高,本文旨在通过对研究生信息检索能力的调查,为了解研究生信息素质的现状提供了一手分析结果,同时为改善研究生信息素质现状提出一些建设性意见.     

贝叶斯决策方法及其应用

风险决策存在于诸多的生产和经济活动中，在制定一项决策之前，应注重其风险分析，合理地将风险中的信息加以利用。根据贝叶斯理论的基本原理，通过一个具体实例，给出了应用贝叶斯理论的步骤和方法。     

基于动力指纹和Bayes数据融合的桥梁损伤识别

受测量噪声的影响,采用单一指标评价桥梁安全容易产生误判,因此提出一种基于Bayes理论的桥梁损伤识别方法.该方法将识别过程分解为损伤定位识别与损伤定量识别两部分,首先采用Bayes公式融合归一化的动力指纹,进行损伤位置识别,进而提取损伤处的动力指纹构建Bayes网络,计算各节点的条件概率,从而识别损伤程度.通过简支梁数值模拟验证了该方法具有良好的抗噪性,尤其能够对小损伤准确定位,对程度差别小的损伤准确分类.     

基于贝叶斯次优解的锂电池SOC初值追踪研究

在使用循环算法的剩余电量(state of charge,SOC)估算的理论研究中,通常都是设定已知的初始值,无法完全论证算法的各项性能指标.在研究剩余电量估算问题时,则重点针对不确定的初始赋值,使用一种贝叶斯次优解法——粒子滤波(PF),基于SOC动态观测模型,研究其收敛到真值的情况,并辅以MATLAB仿真实验.结果显示在非平台期PF可快速追踪初值,而在平台区由于自身算法的缺陷,无法追踪初值,应根据此区域的电压特性,更换为另一种次优解算法.论证了在解决锂电池初值追踪问题上,两种次优解算法的结合使用可以得到理想结果.     

Strategic asset allocation by mixing shrinkage,vine copula and market equilibrium

We propose a new portfolio optimization method combining the merits of the shrinkage estimation, vine copula structure, and Black–Litterman model. It is useful for many investors to satisfy simultaneously the three investment objectives: estimation sensitivity, asymmetric risks appreciation, and portfolio stability. A typical investor with such objectives is a sovereign wealth fund (SWF). We use China's SWF as an example to empirically test the method based on a 15‐asset strategic asset allocation problem. Robustness tests using subsamples not only show the method's overall effectiveness but also manifest that the function of each component is as expected.     

Hierarchical Shrinkage in Time‐Varying Parameter Models

In this paper, we forecast EU area inflation with many predictors using time‐varying parameter models. The facts that time‐varying parameter models are parameter rich and the time span of our data is relatively short motivate a desire for shrinkage. In constant coefficient regression models, the Bayesian Lasso is gaining increasing popularity as an effective tool for achieving such shrinkage. In this paper, we develop econometric methods for using the Bayesian Lasso with time‐varying parameter models. Our approach allows for the coefficient on each predictor to be: (i) time varying; (ii) constant over time; or (iii) shrunk to zero. The econometric methodology decides automatically to which category each coefficient belongs. Our empirical results indicate the benefits of such an approach. Copyright © 2013 John Wiley & Sons, Ltd.     

维吾尔文网络舆情研究现状及几个关键问题

随着信息化的普及，作为新疆地区主要少数民族文字之一的维吾尔文，在计算机信息处理，标准化等方面，尤其是维吾尔文WEB建设方面有了迅速的发展，而其发展和管理、应用的特殊性，又使得信息复杂多样并且难以控制，因此研究多语种网络信息的安全和正确的舆情疏导机制成为一个重要的课题，本文主要阐述了维吾尔文网络舆情分析目前国内外的研究状况和要解决的几个关键技术问题。     

未来无线移动通信前瞻

随着移动通信技术的发展,世界正在变的越来越小,信息的交流也变的更为顺畅,同时人们对无线移动通信的要求也越来越高。目前4G技术已逐步在世界范围内开始部署,这也激励了科技工作者们继续探索未来无线移动通信的技术。未来无线通信的发展必将面临着非常高的移动终端密度和如何提高频谱利用率等一系列问题,如何克服这些问题,将会是未来移动通信的重点。要解决上述问题,文章尝试着从以下几种相互关联的技术中给出答案：小基站技术、设备与设备互联技术、云端无线接入网络技术和无线网络最优接入技术。     

Forecasting US Recessions with a Large Set of Predictors

In this paper, I use a large set of macroeconomic and financial predictors to forecast US recession periods. I adopt Bayesian methodology with shrinkage in the parameters of the probit model for the binary time series tracking the state of the economy. The in‐sample and out‐of‐sample results show that utilizing a large cross‐section of indicators yields superior US recession forecasts in comparison to a number of parsimonious benchmark models. Moreover, the data‐rich probit model gives similar accuracy to the factor‐based model for the 1‐month‐ahead forecasts, while it provides superior performance for 1‐year‐ahead predictions. Finally, in a pseudo‐real‐time application for the Great Recession, I find that the large probit model with shrinkage is able to pick up the recession signals in a timely fashion and does well in comparison to the more parsimonious specification and to nonparametric alternatives. Copyright © 2016 John Wiley & Sons, Ltd.