盾构法施工风险的多态贝叶斯网络模型分析

地铁隧道具有投资规模大、风险因素多、社会影响大等特点,这决定了在施工过程中进行风险管理的必要性.鉴于风险的模糊性,很难用一个具体的概率值或概率区间来描述其发生概率,由此提出了用多个概率区间来分析盾构法施工过程中的风险.以划分地铁及地下工程风险概率等级的5个概率区间为度量标准,设计出风险因素概率等级调查表,通过专家调查法可统计出盾构法施工风险因素概率等级的概率分布;基于调查表,采用贝叶斯网络建立了分析风险事故概率等级概率分布的多态系统.利用该系统对某地铁隧道工程进行风险评估,得到了该工程的风险概率等级,并分析出相应风险概率等级下进出洞洞口密封效果不好、中轴线偏离过大造成管片破裂以及管片密封材料损伤、破坏等因素为重点控制因素.     

基于中国餐馆过程的语音增强

针对有色噪声中的语音增强问题,通过引入中国餐馆过程混合模型（Chinese Restaurant Process Mixture Model,CRPMM）,其潜变量满足中国餐馆过程,能够较方便地获得马尔科夫链式样本的展开.建立了参变量与潜变量基于块采样的后验更新形式,结合卡尔曼滤波技术,能够在分布空间上更精确地逼近噪声的后验分布.仿真算例及实际语音信号增强算例表明,较之传统的参数化卡尔曼滤波算法及变分贝叶斯滤波算法,基于数据驱动的无穷维的块采样技术能够更好地适应新模态,并取得较好的语音增强效果.     

基于贝叶斯分类的主题爬虫

随着网络的高速发展,其信息资源越来越庞大,面对巨量的信息库,搜索引擎起着重要的作用．主题爬虫技术作为搜索引擎的主要核心部分,计算搜索结果与搜索主题的关系,该关系被称为相关性．一般主题爬虫方法只计算网页内容与搜索主题的相关性,作者所提主题爬虫,通过链接内容和锚文本内容计算链接的重要性,然后利用贝叶斯分类器对链接进行分类,最后利用余弦相似函数计算网页的相关性,如果相关值大于阀值,则认为该网页与预定主题相关,否则不相关．实验结果证明：所提出主题爬虫方法可以获得很高的精确度．     

随机动态条件驾驶员多讫地出行路径选择研究

为解决驾驶员多讫地出行路径选择问题,综合各评价指标,提出了势力圆法,并对不同重要度讫地的势力圆分别进行了讨论研究. 结合离散动态贝叶斯网络,建立了多讫地出行的驾驶员路径选择动态决策模型. 通过具体算例对该模型进行了仿真验证,结果表明,综合势力圆法和离散动态贝叶斯网络方法在多讫地出行的路径决策中有着很好的适用性.      

艾拉姆咖分布均值比的Bayes估计及检验

给出艾拉姆咖分布在定数截尾场合下均值比k的极大似然估计;由"平均剩余寿命"的概念得到k的拟矩估计;取共轭先验分布给出k的经验Bayes估计、区间估计及假设检验;通过实例给出不同截尾样本下k的点估计和区间估计.     

Co-regulated Protein Functional Modules with Varying Activities in Dynamic PPI Networks

Current methods for the detection of differential gene expression focus on finding individual genes that may be responsible for certain diseases or external irritants. However, for common genetic diseases, multiple genes and their interactions should be understood and treated together during the exploration of disease causes and possible drug design. The present study focuses on analyzing the dynamic patterns of co-regulated modules during biological progression and determining those having remarkably varying activities, using the yeast cell cycle as a case study. We first constructed dynamic active protein-protein interaction networks by modeling the activity of proteins and assembling the dynamic co-regulation protein network at each time point. The dynamic active modules were detected using a method based on the Bayesian graphical model and then the modules with the most varied dispersion of clustering coefficients, which could be responsible for the dynamic mechanism of the cell cycle, were identified. Comparison of results from our functional module detection with the state-of-art functional module detection methods and validation of the ranking of activities of functional modules using GO annotations demonstrate the efficacy of our method for narrowing the scope of possible essential responding modules that could provide multiple targets for biologists to further experimentally validate.     

基于规范化的B样条密度模型的聚类算法

针对有参混合模型的聚类算法需要假设模型为某种已知的参数模型, 存在模型不匹配及非参数正交多项式密度估计不是概率密度函数的问题, 提出了一种基于规范化的B样条密度模型的图像聚类算法。通过构建基于规范化的B样条密度函数的非参数混合模型, 利用非参数B样条期望最大(NNBEM: Non parametric B splineExpectation Maximum)算法估计密度模型的未知参数, 并根据贝叶斯准则实现图像的聚类。该方法不需要对模型做任何假设, 可有效克服有参混合模型与实际数据分布不一致问题。对模拟图像和真实图像数据进行仿真的结果表明, 规范化的B样条密度模型的聚类算法比其他算法具有更好的聚类性能。     

Uncertainty quantification of a graphite nitridation experiment using a Bayesian approach

In this paper, a stochastic system based Bayesian approach is applied to estimate different model parameters and hence quantify the uncertainty of a graphite nitridation experiment. The Bayesian approach is robust due to its ability to characterize modeling uncertainties associated with the underlying system and is rigorous due to its exclusive foundation on the axioms of probability theory. We choose an experiment by Zhang et al. [1] whose main objective is to measure the reaction efficiency for the active nitridation of graphite by atomic nitrogen. To obtain the primary physical quantity of interest, we need to model and estimate the uncertainty of a number of other physical processes associated with the experimental setup. We use the Bayesian method to obtain posterior probability distributions of all the parameters relevant to the experiment while taking into account uncertainties in the inputs and the modeling errors. We use a recently developed stochastic simulation algorithm which allows for efficient sampling in the high-dimensional parameter space. We show that the predicted reaction efficiency of the graphite nitridation and its uncertainty is ∼3.1 ± 1.0 × 10⁻³ that is slightly larger than the ones deterministically obtained by Zhang et al. [1].     

基于信息融合技术的结构模型修正研究

提出基于信息融合技术的结构模型修正法.在概率思维条件下,采用二次响应面代替有限元模型,通过调整融合系数实现先验信息和实测信息的融合,并利用响应面自身特性和精度要求进行迭代修正.该方法计算量小,程序化高,易于工程应用,还可推广至非线性等复杂情况.某抽水蓄能电站地下厂房结构的有限元模型修正结果验证了该方法的有效性.     

A pathway from Bayesian statistical analysis to superstatistics

Superstatistics and Tsallis statistics in statistical mechanics are given an interpretation in terms of Bayesian statistical analysis. Subsequently superstatistics is extended by replacing each component of the conditional and marginal densities by Mathai’s pathway model and further both components are replaced by Mathai’s pathway models. This produces a wide class of mathematically and statistically interesting functions for prospective applications in statistical physics. It is pointed out that the final integral is a particular case of a general class of integrals introduced by the authors earlier. Those integrals are also connected to Krätzel integrals in applied analysis, inverse Gaussian densities in stochastic processes, reaction rate integrals in the theory of nuclear astrophysics and Tsallis statistics in nonextensive statistical mechanics. The final results are obtained in terms of Fox’s H-function. Matrix variate analogue of one significant specific case is also pointed out.