区域函数的广义导数及其应用

本文以[7]的基本概念为基础,并根据Clarke的广义导数[1],以及Lasotra和Strauss[6]的多值函数f(x)的广义微分D_f(x)的定义.从而建立了区域函数F(x)的广义导数D_F=∪∩{G(x)?B(R),?x∈B(R);G(x)=F_x=F(x)}讨论了区域函数F(x)的广义导数的存在性;建立了区域函数的广义Fréchet导数存在的必要充分条件.     

随机向量相关性变量的影响函数及其应用

Iafluence function of a statistic describes the effect of a small perturbation of the data on this statistic. This paper studies the influence function of squared correlation coefficient R^2 of k sets of variables defined by Y. Zhang and X. Zhu and gives the variance and its estimation of the squared sample correlation coefficient Rn^2. Finally, the results obtained are applied to assessing the infiuence of individual observation with the aid of an example.     

概率生成函数及其在随机微分方程中的应用

由于概率生成函数和分布函数一一对应,分布函数唯一确定概率生成函数;概率生成函数也唯一确定分布函数,并且使用概率生成函数做为取整数值的离散随机变量的研究工具比用分布函数在许多方面显得更加简便,因此,概率生成函数已成为概率论的重要分析工具之一。     

区域函数

为了把点函数理论、区间函数理论和方法推广到任意区域,作者建立了区域的收缩和区域的保核收缩,区域的扩张和区域的保核扩张等新理论.从这些概念出发,给出了区域函数的新定义,并将区域函数的核(即不动点)与此区域函数的定义区域的稳定中心联系起来,从而建立了区域理论,和区域与区域算法. 在应用中,为了求区域的稳定中心,作者采用了由Hartfiel和其它作者建立的矩阵测度理论;并讨论了与区域相伴的线性代数方程组系数矩阵的测度理论.     

平稳随机函数导论

本文的主要目的是给予平稳随机函数(序列或过程)的外推和滤过问题以尽可能初等的,而又数学上严格的叙述。由于力求作初等的叙述,因而对于这问题最简单的特别情形——有理的(对或对应地对)谱密度存在的情形,将予以较多的注意。至于A.H.柯尔莫哥洛夫关于一般情形的深入理论,本文仅将其结果作简短浏览。本文第一章,包含平稳随机函数分谱理论相当全面的叙述。这一理论是以A.欣金的的著作为基础发展起来的,在今天已构成这领域中差不多全部研     

基于径向基函数的微分求积区域分裂法及其应用

1 引言本文提出的基于径向基函数的微分求积区域分裂法是以径向基函数(RBFs)作为微分求积法(DQM)的基函数,并结合区域分裂法(DDM)提出的,结合了上述三种方法的优点,对解决不规则区域上的问题有很高的实用价值．     

D函数及其应用

定义:设集合A={0,1,2,…,n-1},其中n是正整数;设f:A→A是A到A上的函数,对于i∈A,把i-f(i)被n除所得的余数记作d_i,其中0≤d_i相似文献   

单调函数及其应用

设 f为定义在D上的函数 ,若对于D中任意两个数x1,x2 ,当x1f(x2 )时 ,称 f为D上严格递减函数 .递增函数和递减函数统称为单调函数 ,函数的单调性是函数的重要性质之一 ,利用函数的单调性 ,可以比较函数值的大小 ,证明一些不等式以及解决某些方程问题和函数极值问题 .例 1　证明 |x1+x2 +… +xn|1+|x1+x2 +… +xn|≤ |x1|1+|x1|+|x2 …     

I-J 函数及其应用

三次和四次代数方程求根问题,早有人研究出一些解决办法.任何复系数的三次、四次方程的根,都可以通过其系数经有限次的四则运算和开方运算具体地表达出来.但是,这些公式都比较繁琐,不便于应用.同时,在推导那些公式的时候,由于没有把代数方法同几何背景结合起来,给初学者在理解和记忆这些公式时造成困难.     

随机变量和的模函数分布及其应用

介绍了一个在计算机科学、信息科学等学科中具有广泛应用的随机变量和的模函数,计算了其分布,并提供了该函数在图像信息安全领域的一个应用例子,验证了理论结果.     

Entropy for Random Partitions and Its Applications

Asymptotic properties of partitions of the unit interval are studied through the entropy for random partition

一般随机不动点定理及其应用

在本文中我们得到了一个一般的随机不动点定理,推广了Engl[4,7]和Bocsan[8]的主要结果.这一定理的有用性在于目前由许多作者用特殊方法得到的随机不动点定理[1,4,5-13]均能利用我们的一般定理(定理1和系1,2)得到,最后给出了我们的定理对随机积分和微分方程的应用.     

随机变量和的特征函数的性质与应用

运用随机变量和的特征函数定义研究了随机变量和的特征函数在原点处二阶导数与协方差矩阵的关系,并给了一个简单的应用.     

随机扰动动力系统的局部混合及应用

本文给出了由εｄＢｔ扰动的动力系统ｄＸｔ＝ｂ（Ｘｔ）ｄｔ局部混合性。作为应用，用一种新方法证明了亚稳态行为：指数平整性和指数越出率。     

B值随机元的Rosenthal不等式及其应用

该文证明了一类B值随机元序列的Rosenthal不等式,一些经典的Rosenthal不等式作为其推论被蕴含其中.作为不等式的应用,还给出了行为鞅差随机元的阵列的完全收敛性的一些结果,推广和改进了一些熟知的结论.     

Random Measures and Applications

Abstract

A mapping Z(·) from a δ-ring ?₀(?) into the vector space of random variables L ^p (P) is a vector-valued measure if it is σ-additive in the metric of its range. It is a vector measure if the range is a Banach space and a random measure if also its values are independent on disjoint sets. An important reason for this study is to construct integrals relative to such Zs, which typically do not have finite variation. For this, it is essential to find a controlling (σ-finite) measure for Z that is not available if 0 <p < 1, and here the random measure is taken to be p-stable and utilize properties of infinitely divisible distributions. In the case of p = 2, Z(·) induces a bimeasure, and if p > 2 is an integer it induces a polymeasure, either of which need not be (signed) measures on product spaces. Important applications lead to all these possibilities. In all those cases, a detailed analysis of vector-valued set functions is presented, with special focus for the cases of 0 <p < 1 and p = 2 where probability and Bochner's L ^{2, 2} boundedness plays a key role. Specialization if Z is stationary, harmonizable, and/or isotropic are discussed using the group structure of ? ⁿ , n ≥ 1, extending it for an lca group G. If Z is Banach valued or a quasi-martingale measure, methods of obtaining integrals are outlined in the last section, and open problems motivated by applications are pointed out at various places.     

The Properties and Applications of Invariable Function for Markov Chains in Random Environments

In this paper, various concepts ofrecurrence and transience are introduced into the research fieldof Markov chains in random environments, and the concepts andproperties of invariant function for Markov chains in randomenvironments are investigated. By using those properties, we obtaina criterion for the state to be recurrent or transient.     

On the Average Taxicab Distance Function and Its Applications

Generalized conics are subsets in the space all of whose points have the same average distance from a given set of points (focal set). The function measuring the average distance is called the average distance function (or the generalized conic function). In general it is a convex function satisfying a kind of growth condition as the preliminary results of Sect. 2 show. Therefore any sublevel set is convex and compact. We can also conclude that such a function has a global minimizer.

The paper is devoted to the special case of the average taxicab distance function given by integration of the taxicab distance on a compact subset of positive Lebesgue measure in the Euclidean coordinate space.

The first application of the average taxicab distance function is related to its minimizer. It is uniquely determined under some natural conditions such as, for example, the connectedness of the integration domain. Geometrically, the minimizer bisects the measure of the integration domain in the sense that each coordinate hyperplane passing through the minimizer cuts the domain into two parts of equal measure. The convexity and the Lipschitzian gradient property allow us to use the gradient descent algorithm that is formulated in terms of a stochastic algorithm (Sect. 3) to find the bisecting point of a set in \(\mathbb{R}^{n}\).

Example 1 in Sect. 4 shows the special form of the average taxicab distance function of a convex polygon. The level curves (generalized conics) admit semidefinite representations as algebraic curves in the plane because the average taxicab distance function is piecewise polynomial of degree at most three.

Some applications in geometric tomography are summarized as our main motivation to investigate the concept of the average taxicab distance function. Its second order partial derivatives give the coordinate \(X\)-rays of the integration domain almost everywhere and vice versa: the average taxicab distance function can be expressed in terms of the coordinate \(X\)-rays. Therefore the reconstruction of the sets given by their coordinate \(X\)-rays can be based on the average taxicab distance function instead of the direct comparison of the \(X\)-rays. In general (especially, in some classes of non-convex sets), the convergence property of the average taxicab distance function with respect to the Hausdorff convergence of the integration domain is better than the convergence of the \(X\)-rays (see regular and \(X\)-regular convergences in Sect. 4.1) and we can apply a standard approximation paradigm (Footnote 1) to solve the problem.

In the last section we prove that any compact convex body is uniquely determined by the diagonalization of its covariogram function measuring the average taxicab distance of the points from the intersection of the body with the translates of its axis parallel bounding box.