不等式证明中“数与形的深层对话”

数形结合是重要的数学思想方法,它勾连起代数和几何之间的内在联系,将抽象的代数关系用直观的几何形式表达出来,从而便捷地解决问题．这一点在要求颇高的不等式证明中也有所体现．下面结合具体问题来谈一谈不等式证明中该如何实现“数与形的深层对话”．     

运用概率方法证明某些数学不等式

在数学上一些常见的不等式的证明,若运用代数方法较难得到解决.运用概率方法较方便地证明了某些数学不等式,同时,沟通了不同学科之间的联系.     

随机变量数学期望的一个注记

介绍一种计算随机变量数学期望的方法,利用这种方法容易得到数学期望的相关性质,很多概率与矩的不等式证明也因之变得更为简洁.     

对一类分式不等式的概率证法的反思

文[1]利用概率中有关数学期望的一个性质Eξ2≥E2ξ证明了一类分式不等式,将概率知识与不等式证明联系起来,确实给人以启迪.然而,关于这种较为新颖的证明方法,笔者对文[1]中的某些观点却不敢苟同,下面是笔者对于概率证法的几点反思.1概率证法是“创新证法”么文[1]把这种概率证     

抽象与具体函数积分不等式的证明

在区间I上连续、单调的抽象函数的积分不等式证明的基本思路是适当进行积分变换、分拆积分区间，使不等式恒等变形等手段，使能应用函数的单调性质。具体函数的积分不等式的一般证明方法是把被积函数适当缩放、求出最值(或上下确界)等。     

基于数学核心素养的单元一课时教学设计——以“条件概率”为例

“条件概率与全概率公式”单元教学设计以整体关联性知识为载体,通过具体实例,由特殊到一般,从具体到抽象,建构条件概率,导出条件概率和全概率公式,并在实际运用中促进“一般观念”的发展,实现知识、方法、能力迁移,提升数学核心素养.     

也谈一类竞赛不等式的创新证法

在国内外的数学竞赛中，常出现一些分式不等式的证明问题．这些不等式形式优美、内涵丰富，命题者给出的证法异彩纷呈．文给出了分式不等式的一种创新证法——向量内积法，笔者深受启发．本文通过构造离散型随机变量的概率分布列，利用数学期望的一个性质给出这类不等式的另一种新证法，先给出数学期望的一个性质。     

不等式证明的概率方法

运用概率论中的Jensen不等式,并且适当地构造随机变量的方法证明不等式,使得不等式的证明变得简单、清晰,同时使得不等式具有某种概率统计意义.     

利用概率方法证明等式与不等式

利用概率方法可以证明一些等式与不等式,概率方法有着广泛的应用.     

怎样认识新课标中的基本不等式

1 背景介绍 基本不等式(√ab)≤a+b/2(a＞0,b＞0)是高中数学中最重要的一个不等式.在现行教材编排的体系中,基本不等式首先出现在《数学5》(必修)[1～3],之后在《数学4—5》(选修)[4]又再次出现.必修教材[1～3]对基本不等式的研究,都是从背景引入、抽象提炼、证明方法、几何意义、变式引申、拓展应用等六个方面进行展开的,如《数学5》(必修)[1]的第三章"不等式"的基本不等式,首先以2002年北京市召开的第24届国际数学大会会标(图1)为问题背景引入,提出"你能在这个图中找出相等式或不等式关系吗?"通过抽象概括,提炼出重要不等式a2+b2≥2ab(a,b∈R)并给出几何解释,在此基础上,又通过演绎替换,数形结合,证明探究及实际应用等四种不同的角度引导学生认识基本不等式,最后将此会标作为封面插图[1].     

A probabilistic approach to modelling uncertain logical arguments

Argumentation can be modelled at an abstract level using a directed graph where each node denotes an argument and each arc denotes an attack by one argument on another. Since arguments are often uncertain, it can be useful to quantify the uncertainty associated with each argument. Recently, there have been proposals to extend abstract argumentation to take this uncertainty into account. This assigns a probability value for each argument that represents the degree to which the argument is believed to hold, and this is then used to generate a probability distribution over the full subgraphs of the argument graph, which in turn can be used to determine the probability that a set of arguments is admissible or an extension. In order to more fully understand uncertainty in argumentation, in this paper, we extend this idea by considering logic-based argumentation with uncertain arguments. This is based on a probability distribution over models of the language, which can then be used to give a probability distribution over arguments that are constructed using classical logic. We show how this formalization of uncertainty of logical arguments relates to uncertainty of abstract arguments, and we consider a number of interesting classes of probability assignments.     

A Bernstein—Chernoff deviation inequality, and geometric properties of random families of operators

In this paper we first describe a new deviation inequality for sums of independent random variables which uses the precise constants appearing in the tails of their distributions, and can reflect in full their concentration properties. In the proof we make use of Chernoff's bounds. We then apply this inequality to prove a global diameter reduction theorem for abstract families of linear operators endowed with a probability measure satisfying some condition. Next we give a local diameter reduction theorem for abstract families of linear operators. We discuss some examples and give one more global result in the reverse direction, and extensions. This research was partially supported by BSF grant 2002-006.     

Graph theory and molecular distribution: Part I: copolycondensation of polyfunctional monomers bearing identical reactive groups

The equilibrium distribution for a random f-functional polycondensation system was derived by different probability arguments by Flory, Stockmayer, Good, Gordon, Whittle and others. The extension of these techniques to more complex systems involves abstract mathematics and often requires deriving probability generating functions. This study shows how Graph Theory allows one to obtain directly and readily theses molecular distribution functions in the most general systems of order I, i.e. consisting of various monomer units with different functionalities, all sites being identical and equireactive.     

Complexity and information: Measuring emergence,self-organization,and homeostasis at multiple scales

Concepts used in the scientific study of complex systems have become so widespread that their use and abuse has led to ambiguity and confusion in their meaning. In this article, we use information theory to provide abstract and concise measures of complexity, emergence, self-organization, and homeostasis. The purpose is to clarify the meaning of these concepts with the aid of the proposed formal measures. In a simplified version of the measures (focusing on the information produced by a system), emergence becomes the opposite of self-organization, while complexity represents their balance. Homeostasis can be seen as a measure of the stability of the system. We use computational experiments on random Boolean networks and elementary cellular automata to illustrate our measures at multiple scales. © 2012 Wiley Periodicals, Inc. Complexity, 2012     

Use of the Probability Integral Transformation to Fit Nonlinear Mixed-Effects Models With Nonnormal Random Effects

This article describes a simple computational method for obtaining the maximum likelihood estimates (MLE) in nonlinear mixed-effects models when the random effects are assumed to have a nonnormal distribution. Many computer programs for fitting nonlinear mixed-effects models, such as PROC NLMIXED in SAS, require that the random effects have a normal distribution. However, there is often interest in either fitting models with nonnormal random effects or assessing the sensitivity of inferences to departures from the normality assumption for the random effects. When the random effects are assumed to have a nonnormal distribution, we show how the probability integral transform can be used, in conjunction with standard statistical software for fitting nonlinear mixed-effects models (e.g., PROC NLMIXED in SAS), to obtain the MLEs. Specifically, the probability integral transform is used to transform a normal random effect to a nonnormal random effect. The method is illustrated using a gamma frailty model for clustered survival data and a beta-binomial model for clustered binary data. Finally, the results of a simulation study, examining the impact of misspecification of the distribution of the random effects, are presented.     

Estimating household purchase rates for consumer nondurable goods

Understanding the purchase rates of households for frequently purchased packaged goods is an important element in developing effective marketing strategies. Previous researchers have attempted to estimate these rates by assuming that the time between purchases is a random variable that follows some common parametric probability distribution such as the exponential or Weibull distribution. Recent research has shown that for many frequently purchased packaged goods, the interpurchase times cannot be adequately described by these commonly used probability distributions. In this study we demonstrate how household purchase rates can be estimated in a robust manner using a generalized semiparametric approach that obviates the need for specifying a parametric form for the distribution of interpurchase times. The motivation being that often there is no theory of household purchase behaviour that specifies a priori the probability distribution underlying the interpurchase times. Our empirical results indicate that, for the data analysed, the household purchase rates exhibit a regular pattern that cannot be recovered by probability distributions often used in previous research. Further, marketing actions taken by sellers do indeed influence household purchase behaviour.     

Vectors of two-parameter Poisson-Dirichlet processes

The definition of vectors of dependent random probability measures is a topic of interest in applications to Bayesian statistics. They represent dependent nonparametric prior distributions that are useful for modelling observables for which specific covariate values are known. In this paper we propose a vector of two-parameter Poisson-Dirichlet processes. It is well-known that each component can be obtained by resorting to a change of measure of a σ-stable process. Thus dependence is achieved by applying a Lévy copula to the marginal intensities. In a two-sample problem, we determine the corresponding partition probability function which turns out to be partially exchangeable. Moreover, we evaluate predictive and posterior distributions.     

The local Dirichlet process

As a generalization of the Dirichlet process (DP) to allow predictor dependence, we propose a local Dirichlet process (lDP). The lDP provides a prior distribution for a collection of random probability measures indexed by predictors. This is accomplished by assigning stick-breaking weights and atoms to random locations in a predictor space. The probability measure at a given predictor value is then formulated using the weights and atoms located in a neighborhood about that predictor value. This construction results in a marginal DP prior for the random measure at any specific predictor value. Dependence is induced through local sharing of random components. Theoretical properties are considered and a blocked Gibbs sampler is proposed for posterior computation in lDP mixture models. The methods are illustrated using simulated examples and an epidemiologic application.     

On a New Class of Systems of Generalized Quasi-Variational Inequalities

We introduce new types of systems of generalized quasi-variational inequalities and we prove the existence of the solutions by using results of pair equilibrium existence for free abstract economies. We consider the fuzzy models and we also introduce the random free abstract economy and the random equilibrium pair. The existence of the solutions for the systems of quasi-variational inequalities comes as consequences of the existence of equilibrium pairs for the considered free abstract economies.     

多维随机变量分量间的线性相关性研究

研究了n维随机变量分量间的线性相关性,给出了具有(在概率1意义下)严格线性关系的充要条件以及衡量线性相关性程度的量.