带区间数据的最小风险斯坦纳树问题

考虑了在带区间数据的不确定网络中, 最小风险和模型以及最小最大风险模型下的斯坦纳树问题. 它们推广了相应模型下的最短路问题和最小支撑树问题, 在网络设计中具有更加广泛的应用.我们分别给出了这两个模型下斯坦纳树问题的近似算法, 并对算法性能做了理论分析和证明. 结果显示我们的算法具有优良的常数逼近的性质, 能在多项式时间内算出令人满意的解.     

高维异方差高斯混合罚模型聚类

罚模型聚类实现了在聚类过程中精简变量的目标,同时如何识别聚类的有效变量成了一个新的问题.在这个问题上,已有的研究有成对罚模型,模型处理了各类数据同方差的情况.考察了异方差情况下的变量选择问题,针对异方差数据提出了两种新的模型,并给出模型的解和算法.模拟数据分析结果表明,异方差数据上两个新模型都有更好的表现.     

发现并定位黄球

本文讨论了如何设置红球和蓝球的数量和位置,发现圆柱区域内的黄球并进行定位的问题.我们考虑了一对红球蓝球发现黄球并定位的问题,在此基础上进行扩展,基本解决了黄球的发现并定位的问题.在静止黄球发现问题中,采用了正三角形扩展和正六边形扩展两种方法.在静止黄球的定位问题中,我们结合正三角形和正六边形运用了旋转法和添点法进行扩展.在运动黄球的定位问题中,讨论了体积概率模型和时间概率模型,给出了两种模型的概率求解公式.在系统协同定位模型中,我们给出了发现定位分步模型和周期系统跟踪模型,其中后者在仿真中实现了大于80%的定位性能,该系统可以简单扩展为多目标快速定位问题.此外,文章讨论了精确测量和颜色切换模型,快速定位问题,多目标定位等问题.     

基于变权系数的人才聘用模型

主要是将招聘模型化成标准的指派问题,运用匈牙利算法进行处理.模型一:通过设置一虚拟部门通过上述方法得到最优分配方案.模型二:构建了偏差函数与变权函数,同样构造成一指派问题,得到七种分配方案,然后从中找出最优解.此模型还可推广到多人应聘多个部门的模型.     

生物斑图中Gray-Scott模型的数值求解及参数反演

生物斑图中反应扩散模型数值解及其参数反演的研究是非常有趣和重要的.主要以生物斑图中Gray-Scott模型为研究对象,对其正、反问题进行研究,提出了一种新的算法-DE-ME算法,并通过数值算例模拟验证了其在求解Gray-Scott模型参数反演问题中的可行性及有效性.结果表明此混合算法能快速有效地解决此类反应扩散模型参数反演问题.     

基于卷积神经网络的公安案件文本语义特征提取方法研究

公安案件文本语义特征提取指的是从案件文本中提取案件的作案方式等特征.从本质上说问题是一类特殊的文本分类问题.构建了基于卷积神经网络(CNN)的文本语义特征提取方法框架.构建了CNN文本分类模型;针对多标记特征提取问题,使用问题转换法结合CNN分类方法来提取特征;讨论了分类中不均衡数据带来的问题,改进了CNN模型中的损失函数.实证结果表明:使用的CNN模型对于文本分类的效果优于传统的支持向量机等分类模型;使用问题转换法中的二值相关法结合CNN模型进行多标记语义特征提取准确率较高;改进后的CNN模型更加适合于不均衡数据的分类,宏平均F1值有了显著的提升.     

突发事件中不实信息的竞争扩散研究

基于Gilpin-Ayala扩散模型,建立了突发事件中不实信的竞争扩散与传播模型,并对模型的动态过程进行深入分析.针对突发事件发生后存在多种信息的竞争扩散的问题,模型能给予很好的解释.最后,就相关问题和分析结果,为应急管理者在突发事件中如何引导舆论提供政策建议.     

基于遗传算法的随机模糊立体运输问题

随机模糊立体运输问题的研究是为了解决现实生活中双因素不确定性问题,在遗传算法的基础上,运用可信性理论建立随机模糊运输问题的机会约束规划模型.通过算例进行VC++编程模拟计算,验证了此模型的可行性,最终提出了基于遗传算法解决随机模糊立体运输问题的模型.     

定弦定角在解题中的运用

定弦定角模型在近些年各地中考压轴题中经常出现,此类问题综合性强,常与三角形,四边形等同时出现,考生很容易失分.很多学生遇到此类问题无从下手,主要原因是对相关模型缺乏总结和概括.基于此,本文对定弦定角常见的几个模型进行总结,帮助学生在运用相关模型中掌握知识和技能,从而提高解决数学压轴题的能力.     

基于逻辑关系的数学模型—逻辑模型的理论与分析

用数学模型研究实际问题是现代科学研究的常用方法.通常采用的数学模型是各种方程.但是使用方程作为研究手段也存在着许多问题,例如无法应用于不可计算的或者不具有数量概念的实际情况中,这样许多问题无法加以讨论.以命题为基础,通过数理逻辑的概念和方法,建立了具有实际意义的逻辑模型的一般理论,分析了逻辑模型的一些基本性质.逻辑模型可以看成传统模型的一种推广.     

Poisson-Lindley回归模型基于EM算法的诊断分析(英文)

利用EM算法研究了来自于Lindley分布权重的混合Poisson模型,即Poisson-Lindley回归模型,从而利用基于完全数据似然函数的条件期望进行统计诊断和局部影响分析,得到了几个有用的诊断统计量,并用一个数值实例说明了所得统计量的有效性.     

Index to Volume 9 (2000)

The stochastic approximation EM (SAEM) algorithm is a simulation-based alternative to the expectation/maximization (EM) algorithm for situations when the E-step is hard or impossible. One of the appeals of SAEM is that, unlike other Monte Carlo versions of EM, it converges with a fixed (and typically small) simulation size. Another appeal is that, in practice, the only decision that has to be made is the choice of the step size which is a one-time decision and which is usually done before starting the method. The downside of SAEM is that there exist no data-driven and/or model-driven recommendations as to the magnitude of this step size. We argue in this article that a challenging model/data combination coupled with an unlucky step size can lead to very poor algorithmic performance and, in particular, to a premature stop of the method. This article proposes a new heuristic for SAEM's step size selection based on the underlying EM rate of convergence. We also use the much-appreciated EM likelihood-ascent property to derive a new and flexible way of monitoring the progress of the SAEM algorithm. The method is applied to a challenging geostatistical model of online retailing.     

ZI纵向计数数据模型的影响分析

基于EM算法和Laplace逼近, 本文给出了研究ZI (即含0较多的)纵向计数数据模型的影响分析方法. 为了识别含0较多的分组计数数据中的强影响点, 本文将ZI纵向数据模型中取值为0的数据赋予一定的权重; 而把随机效应看作缺失数据; 在此基础上引入EM算法, 从而应用完全数据对数似然函数的条件期望以及相应的$Q$距离函数进行影响分析; 并进一步应用Laplace逼近方法简化EM算法中的积分计算. 在此基础上, 基于数据删除模型和局部影响分析方法导出了适用于ZI纵向计数数据模型的诊断统计量. 本文也通过实际计数数据的例子验证了诊断统计量的有效性.     

随机缺失数据下的时间序列分析建模

在时间序列建模过程中,数据的缺失会极大地影响模型的准确性,因此对缺失数据的填补尤为重要.选取北京市空气质量指数(AQI)数据。将其随机缺失10%.分别利用EM算法和polyfit直线拟合的方法对缺失值插补,补全数据后建立ARMA模型并作预测分析.结果表明,利用polyfit函数插补法具有较好的结果.     

Adaptive Rejection Metropolis Simulated Annealing for Detecting Global Maximum Regions

A finite mixture model has been used to fit the data from heterogeneous populations to many applications. An Expectation Maximization (EM) algorithm is the most popular method to estimate parameters in a finite mixture model. A Bayesian approach is another method for fitting a mixture model. However, the EM algorithm often converges to the local maximum regions, and it is sensitive to the choice of starting points. In the Bayesian approach, the Markov Chain Monte Carlo (MCMC) sometimes converges to the local mode and is difficult to move to another mode. Hence, in this paper we propose a new method to improve the limitation of EM algorithm so that the EM can estimate the parameters at the global maximum region and to develop a more effective Bayesian approach so that the MCMC chain moves from one mode to another more easily in the mixture model. Our approach is developed by using both simulated annealing (SA) and adaptive rejection metropolis sampling (ARMS). Although SA is a well-known approach for detecting distinct modes, the limitation of SA is the difficulty in choosing sequences of proper proposal distributions for a target distribution. Since ARMS uses a piecewise linear envelope function for a proposal distribution, we incorporate ARMS into an SA approach so that we can start a more proper proposal distribution and detect separate modes. As a result, we can detect the maximum region and estimate parameters for this global region. We refer to this approach as ARMS annealing. By putting together ARMS annealing with the EM algorithm and with the Bayesian approach, respectively, we have proposed two approaches: an EM-ARMS annealing algorithm and a Bayesian-ARMS annealing approach. We compare our two approaches with traditional EM algorithm alone and Bayesian approach alone using simulation, showing that our two approaches are comparable to each other but perform better than EM algorithm alone and Bayesian approach alone. Our two approaches detect the global maximum region well and estimate the parameters in this region. We demonstrate the advantage of our approaches using an example of the mixture of two Poisson regression models. This mixture model is used to analyze a survey data on the number of charitable donations.     

Degradation modeling with subpopulation heterogeneities based on the inverse Gaussian process

This study proposes a random effects model based on inverse Gaussian process, where the mixture normal distribution is used to account for both unit-specific and subpopulation-specific heterogeneities. The proposed model can capture heterogeneities due to subpopulations in the same population or the units from different batches. A new Expectation-Maximization (EM) algorithm is developed for point estimation and the bias-corrected bootstrap is used for interval estimation. We show that the EM algorithm updates the parameters based on the gradient of the loglikelihood function via a projection matrix. In addition, the convergence rate depends on the condition number that can be obtained by the projection matrix and the Hessian matrix of the loglikelihood function. A simulation study is conducted to assess the proposed model and the inference methods, and two real degradation datasets are analyzed for illustration.     

Maximum likelihood estimation from fuzzy data using the EM algorithm

A method is proposed for estimating the parameters in a parametric statistical model when the observations are fuzzy and are assumed to be related to underlying crisp realizations of a random sample. This method is based on maximizing the observed-data likelihood defined as the probability of the fuzzy data. It is shown that the EM algorithm may be used for that purpose, which makes it possible to solve a wide range of statistical problems involving fuzzy data. This approach, called the fuzzy EM (FEM) method, is illustrated using three classical problems: normal mean and variance estimation from a fuzzy sample, multiple linear regression with crisp inputs and fuzzy outputs, and univariate finite normal mixture estimation from fuzzy data.     

Parameter estimation of the WMTD model

The MTD （mixture transition distribution） model based on Weibull distribution （WMTD model） is proposed in this paper, which is aimed at its parameter estimation. An EM algorithm for estimation is given and shown to work well by some simulations. And bootstrap method is used to obtain confidence regions for the parameters. Finally, the results of a real example--predicting stock prices--show that the WMTD model proposed is able to capture the features of the data from thick-tailed distribution better than GMTD （mixture transition distribution） model.     

Space alternating penalized Kullback proximal point algorithms for maximizing likelihood with nondifferentiable penalty

The EM algorithm is a widely used methodology for penalized likelihood estimation. Provable monotonicity and convergence are the hallmarks of the EM algorithm and these properties are well established for smooth likelihood and smooth penalty functions. However, many relaxed versions of variable selection penalties are not smooth. In this paper, we introduce a new class of space alternating penalized Kullback proximal extensions of the EM algorithm for nonsmooth likelihood inference. We show that the cluster points of the new method are stationary points even when they lie on the boundary of the parameter set. We illustrate the new class of algorithms for the problems of model selection for finite mixtures of regression and of sparse image reconstruction.