基于最大熵原理的地铁换乘站乘客流线优化模型

合理的乘客流线设计是地铁换乘站客流组织的关键技术之一。通过引入乘客流线优化测度熵的概念,基于最大熵原理,将乘客流线优化抽象为最大化测度熵的非线性约束优化问题,并构建了乘客流线优化模型。算例结果表明,不同O-D需求下,由于流线方案与换乘站建筑空间布局匹配程度的不同,导致流线方案集合Г的概率分布p(xj)差异显著。     

基于最大熵原理的地铁换乘站乘客流线优化模型

合理的乘客流线设计是地铁换乘站客流组织的关键技术之一.通过引入乘客流线优化测度熵的概念,基于最大熵原理,将乘客流线优化抽象为最大化测度熵的非线性约束优化问题,并构建了乘客流线优化模型.算例结果表明,不同O-D需求下,由于流线方案与换乘站建筑空间布局匹配程度的不同,导致流线方案集合T的概率分布p(xj)差异显著.     

蒙特卡罗模拟在项目财务评价中的应用

把蒙特卡罗方法运用于项目财务评价,可以通过构造数学模型与计算机仿真得到相对精确的评价值概率分布,并对该评价结果的有效性进行一定程度的讨论。     

基于熵权的代建项目风险模糊综合评价

针对代建制中的风险评价问题,本文构建一种基于熵权的模糊综合评价模型对代建制项目进行风险分析评价。根据多级模糊综合评价模型的特点,将其引入到项目风险评价问题中,熵权法的应用使得结果更加客观合理,并通过实例验证其可行性。     

基于改进DP等效电路模型的动力电池最大功率估算方法

纯电动及混合动力汽车需采用多个单体电池进行串并联成组的动力电池包作为其能量的存储单元,考虑到电池组有限的容量、充放电倍率等参数,需通过电池管理系统对动力电池的最大可用充放电功率进行估算,并对电池组的充放电功率进行科学合理的限制,这是保证电池组使用寿命及可靠性的重要功能。实验数据表明,利用改进的DP等效电路模型对动力电池最大功率进行估算能够得到可靠的估算结果。     

最大熵原理在太阳红斑紫外辐射观测中的应用

最大熵原理起源于信息论和统计力学,是基于有限的已知信息对未知分布进行无偏推断的一种数学方法。文章通过对拉萨红斑紫外辐射数据的分析,探讨了借助最大熵原理实现红斑紫外辐射预测的可能性,结果表明,通过最大熵方法得出的理论概率与实测概率符合程度良好。     

变量抽样方法在审计实务中的运用

变量抽样通常用于账户余额或报表项目的实质性测试,它通过样本审查的结果估算被审总体数额,一般用于应收账款、存货或费用等金额的实质性审查。根据由样本推断总体的方法不同,变量抽样又可分为单位平均数估算法、差额估算法和比率估算法。一、单位平均数估算法单位平均数估算法     

基于人工神经网络与主成分分析的建筑工料(工日)估算方法

针对建筑工料(工日)估算问题,文章首先利用主成分分析得到一组新的输入变量,相对于原始输入变量,有效降低了输入维数,且消除了各输入分量之间的相关性,然后以新的输入变量作为改进型BP网络的输入进行训练与估算,得到了一种新的工料(工日)估算方法。仿真结果表明与直接利用BP网络训练估算相比较,采用这种估算方法,估算结果更加准确.     

ADF单位根检验法的替代方法

研究目标：ADF单位根检验法的替代方法——消除趋势检验法。研究方法：通过蒙特卡罗仿真估算了消除趋势法、ADF法和差分法的检验临界值和检验功效;估算了三种方法检验临界值关于样本容量的响应面函数;估算了消除趋势回归中趋势项和截距项的t统计量的检验临界值及其响应面函数。研究发现：在各种情形下,消除趋势法的检验功效均大于ADF法,而且在误差项相关时更为明显,说明消除趋势法优于ADF法,可以完全替代ADF法;在某些情况下,差分法的检验功效较大。研究创新：对ADF检验模型进行了改进,提出了消除趋势检验法。研究价值：在单位根检验时,可以用消除趋势法代替一直以来被广泛采用的ADF法,提高单位根检验的功效。     

不同资产评估方法评估结果的趋同性分析

资产评估是对资产现行价值进行评定估算的一种专业活动.资产评估方法是实现评定估算资产价值的技术手段.资产评估方法按分析原理可以归纳为三种基本类型,即国际公认的三大评估方法:市场法、收益法和成本法.市场法通过对与被评估资产相似或可替代资产已交易价格的分析,采用必要的比较程序,估算被评估资产的价值;收益法通过分析被评估资产的相关收入和成本费用,将未来净收益折现以估算被评估资产的价值;成本法则首先估算被评估资产的重置成本,然后估算被评估资产已存在的各种损耗或贬值,并将其从重置成本中予以扣除而得被评估资产的价值.     

Information theoretic methods of spatial model building: A guide to the unbiased estimation of the form of probability distributions2

The methods of maximum entropy and minimum information are employed in order to provide unbiased a priori estimates of the form of probability distributions, given limited prior knowledge. These a priori estimates, in turn, can be cast in the form of theoretical models of the phenomena of interest; models to be tested against real world data. As such, the methods of maximum entropy and minimum information have relevance to any scientific or social-scientific discipline wherein the mathematical modelling of probability distributions is a concern. At the same time, however, the methods are mathematically difficult and conceptually demanding. This paper therefore presents a pedagogic introduction to the methods of maximum entropy and minimum information in the hope of making them more accessible to those social scientists who might find them relevant to their work. Using a simple example, the paper works through the two methods, one at a time, adding more and more information to the estimation problem as the discussion proceeds. The focus herein is a geographic one, yet the example employed is easily transferable to alternative contexts.     

基于熵权的TOPSIS法配送中心选址方案评价研究

对选为配送中心住址的评价属于属性权重信息不完全的多属性决策问题,根据传统的TOPSIS方法的基本思路。提出了一种依据数据本身携带的信息确定权重的方法,其核心是通过决策矩阵求熵权,构造加权规范阵得到理想解并哦负的理想解．求得每个方案与正、负理想方案间的欧式距蓠,进而计算出每个方案的综合评价指数,获得所有方案的排序结果。最后,进行实例分析,说明了该方法的实用性和有效性。     

A product Pareto distribution

The Pareto distributions are becoming increasing prominent in several applied areas. In this note, a new Pareto distribution is introduced. It takes the form of the product of two Pareto probability density functions. Various structural properties of this distribution are derived, including its cumulative distribution function, moments, mean deviation about the mean, mean deviation about the median, entropy, asymptotic distribution of the extreme order statistics, method of moments estimates, maximum likelihood estimates and the Fisher information matrix. The calculations involve the use of several special functions.     

高校餐厅服务能力提升研究——基于IE与Flexsim仿真

根据高校餐厅实例,为了提高服务能力,使用工业工程方法对购餐流程、餐厅布局进行优化改善。利用Flexsim软件建立仿真模型,通过实地测量、随机抽样等方法收集数据,通过极大似然估计法、拟合优度检验法验证数据分布并求出仿真参数。仿真结果显示,一小时内优化后的购餐仿真模型较现行模型能够多服务124人,且4个结账台的工作率均得到提升,证明优化后方案能够有效缓解人流拥挤,提高服务能力,对优化方案的实施提供数据支撑。     

Maximum entropy measurement error estimates of singular covariance matrices in undersized samples

Theil and Laitinen (1980) proposed a maximum entropy (ME) technique to estimate singular moment matrices arising from undersized samples. They fitted a continuous ME distribution to the values of a variable arranged in an ascending order of magnitude. In this paper we relax their continuity assumption and simplify matters from a practical viewpoint. Our continuity is restricted to a range around each observation arising from measurement errors. We propose measurement error maximum entropy (MEME or ME2) distribution yielding a positive definite estimate of the dispersion matrix for undersized samples. Some desirable properties of ME2 distribution are mentioned. An example illustrates the simplicity of our ME2 estimation and shows that our estimates can be closer to the true values.     

商业地产项目全寿命周期成本的价值分析决策模型

应用全寿命周期成本的价值分析技术,将寿命周期中各项成本不确定性也作为衡量方案优劣程度的因素。根据各项成本概率分布,利用蒙特卡罗仿真技术计算各方案全寿命成本的分布函数,利用价值分析结果选择最优方案。克服了传统分析方法考虑因素单一、实际值和期望值存在较大偏差的缺点。在商业地产项目方案投资决策中具有一定的应用价值。     

Information Measures in Perspective

Information-theoretic methodologies are increasingly being used in various disciplines. Frequently an information measure is adapted for a problem, yet the perspective of information as the unifying notion is overlooked. We set forth this perspective through presenting information-theoretic methodologies for a set of problems in probability and statistics. Our focal measures are Shannon entropy and Kullback–Leibler information. The background topics for these measures include notions of uncertainty and information, their axiomatic foundation, interpretations, properties, and generalizations. Topics with broad methodological applications include discrepancy between distributions, derivation of probability models, dependence between variables, and Bayesian analysis. More specific methodological topics include model selection, limiting distributions, optimal prior distribution and design of experiment, modeling duration variables, order statistics, data disclosure, and relative importance of predictors. Illustrations range from very basic to highly technical ones that draw attention to subtle points.     

GMM estimation of a maximum entropy distribution with interval data

We develop a generalized method of moments (GMM) estimator for the distribution of a variable where summary statistics are available only for intervals of the random variable. Without individual data, one cannot calculate the weighting matrix for the GMM estimator. Instead, we propose a simulated weighting matrix based on a first-step consistent estimate. When the functional form of the underlying distribution is unknown, we estimate it using a simple yet flexible maximum entropy density. Our Monte Carlo simulations show that the proposed maximum entropy density is able to approximate various distributions extremely well. The two-step GMM estimator with a simulated weighting matrix improves the efficiency of the one-step GMM considerably. We use this method to estimate the U.S. income distribution and compare these results with those based on the underlying raw income data.     

Well-posedness of measurement error models for self-reported data

This paper considers the widely admitted ill-posed inverse problem for measurement error models: estimating the distribution of a latent variable $X^1$ from an observed sample of $X$, a contaminated measurement of $X^1$. We show that the inverse problem is well-posed for self-reporting data under the assumption that the probability of truthful reporting is nonzero, which is supported by empirical evidences. Comparing with ill-posedness, well-posedness generally can be translated into faster rates of convergence for the nonparametric estimators of the latent distribution. Therefore, our optimistic result on well-posedness is of importance in economic applications, and it suggests that researchers should not ignore the point mass at zero in the measurement error distribution when they model measurement errors with self-reported data. We also analyze the implications of our results on the estimation of classical measurement error models. Then by both a Monte Carlo study and an empirical application, we show that failing to account for the nonzero probability of truthful reporting can lead to significant bias on estimation of the latent distribution.