△(G)=3的外平面图的邻强边染色

对图Ｇ（Ｖ,Ｅ）,一正常ｋ－边染色ｆ称为Ｇ（Ｖ,Ｅ）的一邻强边染色,当且仅当对任意ｕｖ∈Ｅ（Ｇ）有ｆ［ｕ］≠ｆ［ｖ］．其中ｆ［ｕ］＝｛ｆ（ｕｗ）｜ｕｗ∈Ｅ（Ｇ）｝,ｆ（ｕｗ）表示染边ｕｗ的色,并称ｘａｓ（Ｇ）＝ｍｉｎ｛ｋ｜存在Ｃ的一ｋ种色的郁强边染色｝为Ｇ的邻强边色数．本文证明了对△（Ｇ）＝３的２－连通外平面图,有ｘａｓ（Ｇ）＝４．     

4连通图的可去边与4连通图的构造

本文引进了４连通图的可去边的概念,,并证明了４连通图Ｇ中不存在可去边的充要条件是Ｇ＝Ｃ５或Ｃ６,同时给出了ｎ阶４连通图的一个新的构造方法．     

定常Satokes方程的窄边四边形有限元法

本文提出了一个试探函数不满足divν＝0的定常Stokes方程的窄边四边形有限元法，利用窄边四边形等参有限元插值定理和Falk的思想，得到了H^1(Ω）模误差的最优收敛估计阶O（h）。     

一类反应扩散问题正解的存在性及渐近性态

本文讨论ｕｔ－△ｕ＝－ｈ２ｇ（ｕ）在Ｒｕｂｉｎ边值条件下初边值问题正解的存在性与Ｔｈｉｅｌｅ模量ｈ间的关系，这里ｇ（ｕ）～ｕ－ｐ（Ｐ∈（０，１））；同时考察当ｔ→＋∞时其正解与相应椭园方程之正解的关系．     

浅议函数的概念

有关函数及其图象的问题,常存在一些不科学的提法,例如：１．“函数在其定义域内没有反函数,而在它的单调区间上存在反函数”．２．“在同一坐标系中,函数ｙ＝ｆ（ｘ）和它的反函数ｘ＝ｆ－１（ｙ）的图象本来是同一个图象,当我们改为习惯写法ｙ＝ｆ－１（ｘ）之后,...     

离散型随机变量密度函数的定义与探讨

使用“δ函数”定义离散型随机变量的密度函数，寻求离散型随机变量与连续型随机变量的统一处理方法．基于离散型随机变量密度函数的定义．其一维随机变量函数的密度函数以及多维随机变量的边缘密度等，均可直接利用连续型随机变量的相关结论．     

图的局部减边控制数

引入局部减边控制函数和局部减边控制数的概念,得到了图的最小局部减边控制函数的性质,给出了局部减边控制数的最好上下界,确定了一些特殊图的局部减边控制数.最后得到了图的减边控制数的最好上界.     

关于μ-全纯函数的契边定理

本文利用Frobenius-Nirenberg定理,以及μ-全纯函数满足Hartogs现象这样的性质,证明了关于μ-全纯函数的契边定理.     

关于μ-全纯函数的契边定理

本文利用Frobenius-Nirenberg定理,以及μ-全纯函数满足Hartogs现象这样的性质,证明了关于μ-全纯函数的契边定理。     

图上合作博弈和图的边密度

1977年, Myerson建立了以图作为合作结构的可转移效用博弈模型(也称图博弈), 并提出了一个分配规则, 也即"Myerson 值", 它推广了著名的Shapley值. 该模型假定每个连通集合(通过边直接或间接内部相连的参与者集合)才能形成可行的合作联盟而取得相应的收益, 而不考虑连通集合的具体结构. 引入图的局部边密度来度量每个连通集合中各成员之间联系的紧密程度, 即以该连通集合的导出子图的边密度来作为他们的收益系数, 并由此定义了具有边密度的Myerson值, 证明了具有边密度的Myerson值可以由"边密度分支有效性"和"公平性"来唯一确定.     

模糊密度随机变量的数学描述

研究了由于概率密度函数的模糊性而引起的模糊概率随机变量问题。给出了区间密度函数、模糊密度函数、模糊密度随机变量及其分布函数和模糊密度随机变量的模糊数学期望、模糊方差等基本概念及定义和计算方法,并证明了有关定理。     

Natural gradient algorithm for stochastic distribution systems with output feedback

In this paper, we use natural gradient algorithm to control the shape of the conditional output probability density function for the stochastic distribution systems from the viewpoint of information geometry. The considered system here is of multi-input and single output with an output feedback and a stochastic noise. Based on the assumption that the probability density function of the stochastic noise is known, we obtain the conditional output probability density function whose shape is only determined by the control input vector under the condition that the output feedback is known at any sample time. The set of all the conditional output probability density functions forms a statistical manifold (M), and the control input vector and the output feedback are considered as the coordinate system. The Kullback divergence acts as the distance between the conditional output probability density function and the target probability density function. Thus, an iterative formula for the control input vector is proposed in the sense of information geometry. Meanwhile, we consider the convergence of the presented algorithm. At last, an illustrative example is utilized to demonstrate the effectiveness of the algorithm.     

二维连续型随机变量函数的分布密度的计算

二维连续型随机变量函数的密度函数的计算既是概率论教学中的一个重点,又是一个难点.本文介绍了一般二维连续型随机变量函数的分布密度的计算方法,并给出了一个新的方法——密度函数转化法.     

单轴拉伸条件下细观非均匀性岩石损伤局部化和应力应变关系分析

岩石在拉应力状态下的力学特性不同于压应力状态下的力学特性．利用细观力学理论研究了细观非均匀性岩石拉伸应力应变关系包括:线弹性阶段、非线性强化阶段、应力降阶段、应变软化阶段.模型考虑了微裂纹方位角为Weibull分布和微裂纹长度的分布密度函数为Rayleigh函数时对损伤局部化和应力应变关系的影响,分析了产生应力降和应变软化的主要原因是损伤和变形局部化.通过和实验成果对比分析验证了模型的正确性和有效性．     

压应力状态下细观非均匀性岩石的损伤局部化和应力应变关系分析

利用摩擦弯折裂纹模型研究了受压条件下细观非均匀性岩石的损伤局部化问题和全过程应力应变关系．模型考虑了裂纹相互作用对损伤局部化和全过程应力应变关系的影响,确定了损伤局部化发生的条件,分析了产生损伤局部化的原因．研究表明全过程应力应变关系包括线弹性阶段、非线性强化阶段、应力降和应变软化阶段．通过和实验对比分析验证了模型的正确性和有效性．     

Some Finite Time Ruin Problems in Markov-Modulated Risk Model

This paper studies the distribution of finite-time ruin quantities. It gives the probability mass function of finite time number of claims, and find the distribution function of aggregate claims by using discretise method and compared with exact distribution function, which shows that the approximation works very well. In addition, by applying decomposition for density function, it gives the explicit expression for joint density of ruin time and deficit at ruin.     

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??This paper studies the distribution of finite-time ruin quantities. It gives the probability mass function of finite time number of claims, and find the distribution function of aggregate claims by using discretise method and compared with exact distribution function, which shows that the approximation works very well. In addition, by applying decomposition for density function, it gives the explicit expression for joint density of ruin time and deficit at ruin.     

一个柔性边界条件约束下的随机变量的分布

讨论n个受柔性边界条件约束的随机变量的概率分布．理论解显示其概率密度函数随变量值增大而减小,当n趋於无穷大时收敛于Delta函数．在有序统计的理论框架下,同时得到最小值分布的解析解．     

A Variational Approach to Dynamic Recrystallization Using Probability Distributions

We investigate a model of dynamic recrystallization in polycrystalline materials. A probability distribution function is introduced to characterize the state of individual grains by grain size and dislocation density. Specifying free energy and dissipation within the polycrystalline aggregate we are able to derive an evolution equation for the probability density function via a thermodynamic extremum principle. Once the distribution function is known macroscopic quantities like average strain and stress can be calculated. For distribution functions which are constant in time, describing a state of dynamic equilibrium, we obtain a partial differential equation in parameter space which we solve using a marching algorithm. Numerical results are presented and their physical interpretation is given. (© 2013 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Risk aggregation in multivariate dependent Pareto distributions

In this paper we obtain closed expressions for the probability distribution function of aggregated risks with multivariate dependent Pareto distributions. We work with the dependent multivariate Pareto type II proposed by Arnold (1983, 2015), which is widely used in insurance and risk analysis. We begin with an individual risk model, where the probability density function corresponds to a second kind beta distribution, obtaining the VaR, TVaR and several other tail risk measures. Then, we consider a collective risk model based on dependence, where several general properties are studied. We study in detail some relevant collective models with Poisson, negative binomial and logarithmic distributions as primary distributions. In the collective Pareto–Poisson model, the probability density function is a function of the Kummer confluent hypergeometric function, and the density of the Pareto–negative binomial is a function of the Gauss hypergeometric function. Using data based on one-year vehicle insurance policies taken out in 2004–2005 (Jong and Heller, 2008) we conclude that our collective dependent models outperform other collective models considered in the actuarial literature in terms of AIC and CAIC statistics.