随机度量理论及其应用在我国最近进展的综述

本旨在全面综述随机度量理论及其应用过去十年在我国发展过程中所获得的主要结果与思想方法。全由十节组成,第一节对我们工作的背景－概率度量空间与随机度量空间理论和一简单的介绍;第二节给出某些有关随机泛函分析及取值于抽象空间的可测函数的预备知识;第三节阐明随机泛函分析与原始随机度量理论（本称之为F－随机度量理论）的整体关系：主要结果是在随机元生成空间给出自然且合理的随机度量与随机范数的构造,从而将随机元与随机算子理论的研究纳入随机度量理论框架;主要思想是将随机泛函分析视为随机度量空间体系上的分析学而统一地发展,从而形成了发展随机泛函分析的一个新的途径－空间随机化途径;除此之外,在本节我们也从随机过程理论观点出发首次提出对应于随机度量理论原始版本的一种新的随机共轭空间理论（叫作F－ 随机共轭空间理论）,它的突出优点是能保持象随机过程的样本性质这样更精细的特性（本节由作的工作构成）;在第四节,基本作最近提出的随机度量理论的一个新的版本（本称之为E－随机度量理论）,从传统泛函分析的角度对过去已被发展起来的随机共轭空间理论（本称之为E－随机共轭空间理论）,从传统泛函分析的角度对过去已被发展起来的随机共轭空间理论（本称之为E－随机共轭空间理论）的基本结果进行系统整理并给以全新的处理（本节内容整体上由作最近后篇论构成,也尤其提到朱林户等人的重要工作）;在本节我们也相当的篇幅论述F－随机共轭空间理论与E－随机共轭空间理论的内存关系与本质差异。在下紧跟的两节,致力于E－随机共轭空间理论深层次的结果,尤其突出了E－随机赋范模与传统的赋范空间、E－随机共轭空间与经典共轭空间之间的内存联系;在第五节给出了几类E－随机赋范模的E－随机共轭空间的表示定理（主要由作的工作,作与游兆永及林熙合作的工作,还有巩馥州与刘清荣合作的工作组成）;在第六节给出完备E－随机赋范模为随机自反的特征化定理（主要由作及合作的工作组成）;在第六节给出完备E－随机赋范模为随机自反的特征化定理（主要由作及合作的工作组成）。尤其在第五及第六节中,我们给出随机度量理论在随机泛函分析及经典Banach空间中若干实质性的应用;第七节简要给出E－随机赋半范模及E－随机对偶系理论初步;第八节简单阐明随机度量理论与泛函分析的关系;第九节阐明了随机度量理论与概率度量空间理论的关系。最后在第十节结合随机度量理论,Banach空间理论及随机泛函分析对发展随机泛函分析的空间随机化途径的合理性与优越性作了进一步的分析。     

随机度量理论及其应用在我国最近进展的综述

本旨在全面综述随机度量理论及其应用过去十年在我国发展过程中所获得的主要结果与思想方法,本由十节组成,第一节对我们工作的背景-概率度量空间与随机度量空间理论作一简单的介绍;第二节给出某些有关随机泛函分析及取值于抽象空间的可测函数的预备知识,第三节阐明随机泛函分析与原始随机度量理论（本称之为F-随机度量理论）的整体关系,主要结果是在随机元生成空间上给出自然且合理的随机度量与随机范数的构造,从而将随机元与随机算子理论的研究纳入随机度量理论框架,主要思想是将随机泛函分析视为随机度量空间体系上的分析学而统一地发展;从而形成了发展随机泛函分析的一个新的途径-空间随机化途径;除此之外,在本节我们也从随机过程理论的观点出发首次提出对应于随机度量理论原始版本的一种新的随机共轭空间理论（叫作F-随机共轭空间理论）,它的突出优点是能保持象随机过程的样本性质这样更精细的特性（本节由作的工作构成）,在第四节,基于作最近提出的随机度量理论的一个新的版本（本称之为E-随机度量理论）,从传统泛函分析的角度对过去已被发展起来的随机共轭空间理论（本称之为E-随机共轭空间理论）的基本结果进行系统整理并给以全新的处理（本节内容整体上由作最近的一篇论构成,也尤其提到朱林户等人的重要工作）,在本节我们也以相当的篇幅论述F-随机共轭空间理论与E-随机共轭空间理论的内在关系与本质差异,在下面紧跟的两节,致力于E-随机共轭空间理论深层次的结果,尤其突出了E-随机赋范模与传统的赋范空间、E-随机共轭空间与经典共轭空间之间的内在联系;在第五节给出了几类E-随机赋范模的E-随机共轭空间的表示定理（主要由作的工作,作与游兆水及林熙合作的工作,还有巩馥州与刘清荣合作的工作组成）,在六节给出完备E-随机赋范模为随机自反的特征化定理（主要由作及合作的工作组成）,尤其是第五及第六节中,我们给出随机度量理论在随机泛函分析及经典Banach空间中若干实质性的应用;第七节简要给出E-随机赋半范模及E-随机对偶系理论初步;第八节简单阐明随机度量理论与泛函分析的关系;第九节简单阐明了随机度量理论与概率度量空间理论的关系,最后在第十节结合随机度量理论,Banach空间理论及随机泛函分析对发展随机泛函分析的空间随机化途径的合理性与优越性作了进一步的分析。     

Jensen's inequality for random elements in metric spaces and some applications

Jensen's inequality is extended to metric spaces endowed with a convex combination operation. Applications include a dominated convergence theorem for both random elements and random sets, a monotone convergence theorem for random sets, and other results on set-valued expectations in metric spaces and on random probability measures.     

Several applications of the theory of random conjugate spaces to measurability problems

The central purpose of this paper is to illustrate that combining the recently developed theory of random conjugate spaces and the deep theory of Banach spaces can, indeed, solve some difficult measurability problems which occur in the recent study of the Lebesgue (or more general, Orlicz)-Bochner function spaces as well as in a slightly different way in the study of the random functional analysis but for which the measurable selection theorems currently available are not applicable. It is important that this paper provides a new method of studying a large class of the measurability problems, namely first converting the measurability problems to the abstract existence problems in the random metric theory and then combining the random metric theory and the relative theory of classical spaces so that the measurability problems can be eventually solved. The new method is based on the deep development of the random metric theory as well as on the subtle combination of the random metric theory with classical space theory.     

Metrization of the Gromov–Hausdorff (-Prokhorov) topology for boundedly-compact metric spaces

In this work, it is proved that the set of boundedly-compact pointed metric spaces, equipped with the Gromov–Hausdorff topology, is a Polish space. The same is done for the Gromov–Hausdorff–Prokhorov topology. This extends previous works which consider only length spaces or discrete metric spaces. This is a measure theoretic requirement to study random boundedly-compact pointed (measured) metric spaces, which is the main motivation of this work. In particular, this provides a unified framework for studying random graphs, random discrete spaces and random length spaces. The proofs use a generalization of the classical theorem of Strassen, presented here, which is of independent interest. This generalization provides an equivalent formulation of the Prokhorov distance of two finite measures, having possibly different total masses, in terms of approximate couplings. A Strassen-type result is also provided for the Gromov–Hausdorff–Prokhorov metric for compact spaces.     

Exchangeable processes need not be mixtures of independent,identically distributed random variables

Summary According to a theorem of de Finetti's, an exchangeable stochastic process with values in a compact metric space can be represented as a mixture of sequences of independent, identically distributed random variables. This paper demonstrates the existence of a separable metric space for which the conclusion fails. In the opposite direction, an example is given of a nonstandard space for which the representation necessarily holds.Modifications of the argument lead to examples of exchangeable stochastic processes and stationary Markov processes which take values in a separable metric space but do not satisfy the conclusions of the Kolmogorov consistency theorem.Research partially supported by National Science Foundation Grant MCS 77-01665     

On large deviation principles in metric spaces

Many articles deal with large deviation principles (LDPs) (see [1-4] for instance and the references in [3,4]), studying mainly the LDP for the sums of random elements or for various stochastic models and dynamical systems. For a sequence of random elements in a metric space, in studying LDPs it turns out natural to introduce the concepts of the local LDP and extended LDP. They enable us to state and prove LDP-type statements in those cases when the usual LDP (cf. [3,4]) fails (see [5,6] and Section 6 of this article). We obtain conditions for the fulfillment of the extended LDP in metric spaces. The main among these conditions is the fulfillment of the local LDP. The latter is usually much simpler to prove than the extended LDP.     

Strong Law of Large Numbers for Stationary Sequences of Random Upper Semicontinuous Functions

Abstract

The strong laws of large numbers with the convergence in the sense of the uniform Hausdorff metric for stationary sequences of random upper semicontinuous functions is established. This approach allows us to deduce many results on the convergence in uniform Hausdorff metric of random upper semicontinuous functions from the relevant results on real-valued random variables that appear as their support functions.     

The number of endpoints of a random walk on a semi-infinite metric path graph

Theoretical and Mathematical Physics - We study a semi-infinite metric path graph and construct the long-time asymptotic logarithm of the number of possible endpoints of a random walk.     

A representation of random upper semicontinuous functions

In this paper a representation of random upper semicontinuous functions in terms of -valued random elements is stated. This fact allows us to consider for the first time a complete and separable metric, the Skorohod one, on a wide class of upper semicontinuous functions. Finally, different relevant concepts of measurability for random upper semicontinuous functions are studied and the relationships between them are analyzed.

     

Resolvent metric and the heat kernel estimate for random walks

In this paper we introduce the resolvent metric, the generalization of the resistance metric used for strongly recurrent walks. By using the properties of the resolvent metric we show heat kernel estimates for recurrent and transient random walks.     

无穷迭代函数系统的遍历定理

度量空间的压缩映射的一个集合称为一个迭代函数系统．凝聚迭代函数系统可以被看成无穷迭代函数系统．研究了紧度量空间上的无穷迭代函数系统．利用Banach极限的特性和均匀压缩性,证明了紧度量空间上无穷迭代函数系统的随机迭代算法满足遍历性．于是,凝聚迭代函数系统的随机迭代算法也满足遍历性．     

Convergence in distribution of random metric measure spaces (Λ-coalescent measure trees)

We consider the space of complete and separable metric spaces which are equipped with a probability measure. A notion of convergence is given based on the philosophy that a sequence of metric measure spaces converges if and only if all finite subspaces sampled from these spaces converge. This topology is metrized following Gromov’s idea of embedding two metric spaces isometrically into a common metric space combined with the Prohorov metric between probability measures on a fixed metric space. We show that for this topology convergence in distribution follows—provided the sequence is tight—from convergence of all randomly sampled finite subspaces. We give a characterization of tightness based on quantities which are reasonably easy to calculate. Subspaces of particular interest are the space of real trees and of ultra-metric spaces equipped with a probability measure. As an example we characterize convergence in distribution for the (ultra-)metric measure spaces given by the random genealogies of the Λ-coalescents. We show that the Λ-coalescent defines an infinite (random) metric measure space if and only if the so-called “dust-free”-property holds.     

Polar sets for anisotropic Gaussian random fields

This paper studies polar sets for anisotropic Gaussian random fields, i.e. sets which a Gaussian random field does not hit almost surely. The main assumptions are that the eigenvalues of the covariance matrix are bounded from below and that the canonical metric associated with the Gaussian random field is dominated by an anisotropic metric. We deduce an upper bound for the hitting probabilities and conclude that sets with small Hausdorff dimension are polar. Moreover, the results allow for a translation of the Gaussian random field by a random field, that is independent of the Gaussian random field and whose sample functions are of bounded Hölder norm.     

EXISTENCE THEOREMS FOR RANDOM MEASURES

Two existence theorems of random measures on a separable complete metric space areproved.It seems that the theory of random measures in locally compact spaces and that inseparable complete metric spaces are essentially different by noting that the critera fortightness of the locally finite measures is much more tedious than that of the Radommeasures.     

随机泛函分析中可交换映象的随机不动点定理

随机不动点定理在随机泛函分析中是一重要问题.在可分完备的度量空间中的随机不动点定理Bharucha-Reid,王梓坤,?pa?ek,Han?,Itoh及作者等都曾进行过讨论(见[1-5,15-20,21]).在本文中我们对概率分析中可交换映象的随机不动点定理得出了几个新的结果,它推广了前述诸人工作中某些重要结果.在确定性情形也推广了Jungck[6,7,8],Das,Naik[9],Rhoades[10],及Ciric[11]的结果.     

Upper semicontinuity of pullback attractors for multi-valued random cocycle

In this paper we study the upper semicontinuity of random attractors for multi-valued random cocycle when small random perturbations approach zero or small perturbation for random cocycle is considered. Furthermore, we consider the upper semicontinuity of random attractors for multi-valued random cocycle under the condition which the metric dynamical systems is ergodic.     

On the rate of convergence in a limit theorem for random symmetric polynomials

In terms of the mean metric, a convergence rate estimate is obtained in a limit theorem for random symmetric polynomials. Supported by the Russian Foundation for Fundamental Research (grant No. 96-01-01920) and by the Hungarian National Foundation for Scientific Research (grant No. T 16665). Proceedings of the Seminar on Stability Problems for Stochastic Models, Moscow, Russia, 1996, Part 11.     

关于随机赋范空间与随机内积空间的某些基本理论(英文)

首先提出随机度量空间定义的另一个提法,这提法不仅等价于原始的定义而且也使随机度量空间自动归入广义度量空间的框架,也考虑了关于拓扑结构的某些新的问题;循着同样的思路,对随机赋范空间的定义也作了新的处理并同时简化了随机赋范模的定义．其次本文也证明了一个Ｅ－范空间的商空间等距同构于一个典型的Ｅ－范空间;进一步,在概率赋范空间的框架下证明了一个概率赋伪范空间是伪内积生成空间的充要条件是它等距同构于一个Ｅ－内积空间,这回答了Ｃ．Ａｌｓｉｎａ与Ｂ．Ｓｃｈｗｅｉｚｅｒ等人新近提出的公开问题．最后,本文转向了它的中心部分──关于随机内积空间的研究,对随机内积空间中的特有且复杂的正交性作较系统的讨论,论证了只有几乎处处正交性才是唯一合理的正交性概念,在此基础上本文尤其将Ｇ．Ｓｔａｍｐａｃｃｈｉａ的在众多学科中都有多种用途的一般投影定理（或称变分不等式解存在性定理）以适当形式推广到完备实随机内积模上．     

Expected Distances between Two Uniformly Distributed Random Points in Rectangles and Rectangular Parallelpipeds

In this paper, the expected distance between two uniformly distributed random points in a rectangle or in a rectangular parallelepiped is computed under three different metrics: the Manhattan metric, the Euclidean metric, and the Chebychev metric.