局部紧拓扑半群上概率测度卷积幂的若干极限定理

我们通过研究极限测度的不变性质，讨论了局部紧拓扑半群上概率测度卷积幂的若干极限性状．推广了[1]－[3]中的若干结果．     

关于遍历拟不变测度

本文主要运用文献[1]的想法,讨论了遍历拟不变测度相应的0—1律,和遍历测度乘积的遍历性,证明了拟线性泛函序列必定以概率1收敛或以概率1发散.文中所得结果特别适用于抽象Wiener过程情形.作为应用,我们推广了Gaussian测度的Landau-Shepp定理和L.Schwartz关于Gaussian柱概率σ-可加性准则.     

Pettis-Aumann积分的若干性质

自文[1]建立了R~n空间的集值映射积分以来,讨论集值映射的可测性、可测选择、集值条件概率、鞅及其Radon-Nikodym定理等方面的工作相继出现.文[6]又系统地研究了R~n空间中集值测度的凸性定理、选择性定理以及与Aumann积分的关系.文[7]则讨论了集值测度的生成定理.但讨论Pettis-Aumann积分的工作目前尚未见到.     

逻辑系统G3在非均匀概率空间下命题的真度理论

在离散概率测度空间下定义了三值逻辑（p,q,r）测度，并相应地定义了命题逻辑系统中公式的真度概念；在三值逻辑（1/6．1/3．1/2）测度和（1/7．2/7.4/7）测度下证明了命题逻辑系统G3中全体公式的真度值之集在[0．1]上是稠密的，并给出真度的表达式；利用真度定义公式的相似度和一种伪距离，为—般离散概率空间下三值命题的近似推理理论提供一种可能的框架．     

地震迁移统计预报的改进

在学习了[1]的基础上。我们用转移概率矩阵来近似代替概率,并在预报测度中加上小地区二重转移概率,另外,还用优选法中的常数代替原来的加权方法,并研究了1900—l970年我国中部南北地震带六级以上地震迁移的统计规律,得到了较好的效果(见表9),还对全国五个主要地震区大地震(M_s≥6)迁移也进行了分析。 用二阶平稳马尔科夫模型对地震迁移作出的统计预报,主要是构造一个预报测度:     

高斯测度的公息量与高斯通路的容量

一、引言在随机过程理论众多的应用中,相当一部分可以用样本函数空间(诸如 C[a,b],L~p,l~p,c_0等)上的概率测度的性质来描述.Baker 在 [3,4]中,就是用无穷维空间测度的语言,给出了关于 Shannon 信息论的重要结果.但这些结果的陈述与证明都是对 Hilbert 空间给出的,仅在 [4] 的结尾给出推广到 Banach 空间的途径,但没有给出相应的陈述(按此途径,定理条件仍要化到相应的 Hilbert 空间上去,致使条件更为复杂).本文用抽象 Wie-ner 空间及其拟线性变换的概念与方法,首先给出关于高斯通路的输入与输出测度     

一般MDP最优策略的唯一性

对于一般的MDP模型，本文证明了对任意一族依赖于历史的随机策略所导致的策略测度类的任意凸组合，存在一个随机马氏策略所导致的策略测度，使得相应于它们的平均期望目标，折扣目标以及期望总报酬目标的值均分别相等，推广了E．B．Dynkin和Yushkevich[1]，M．Puterman[2]，E.Feinberg和A.Shwartz[3]，R．Strauch[4]，以及董泽清和宋京生[5]等相应的所有结果．然后还进一步证明了关于平均期望目标、折扣目标以及期望总报酬目标的最优策略，它们要么唯一，要么有无穷多个．     

混合导数测度的性质及其应用

本文讨论了三维Bernoulli分布与正态分布在混合导数测度下的可压缩性的问题，导出了这两个分布在混合导数测度下可压缩的充分必要条件。同时利用混合导数测度，我们在适当条件下解决了[1]提到的无向图的Gibbs分解问题。     

向量值测度产生的集值测度及性质

由有界变差向量值测度的值域,通过取凸包和闭包,构造了L[0,1],L2[0,1]和C[0,1]空间上的有界变差紧凸集值测度,结果由欧氏空间推广到函数空间.     

Lp(Ω)空间上的Coherent风险度量

本文在Banach空间L^P(Ω)上定义Coherent风险度量ρ：L^P(Ω)→R，证明了ρ是下半连续的Coherent风险度量当且仅当存在Banach空间L^q(Ω)中的一个弱^*闭凸概率测度集Q使得ρ(X)=Z∈Q^sup E(-X/rZ)，其中1/p 1/q=1,推广了[3]中的部分结果。     

A new kind of modified transportation cost inequalities and polynomial concentration inequalities

This paper introduces a new kind of modified transportation cost inequalities and presents some sufficient and necessary conditions for it. Using these conditions, this paper provides some applications of polynomial concentration inequalities to probability measures with no exponential moments. The tensorization property is also provided.     

Divergences without probability vectors and their applications

In general, divergences and measures of information are defined for probability vectors. However, in some cases, divergences are ‘informally’ used to measure the discrepancy between vectors, which are not necessarily probability vectors. In this paper we examine whether divergences with nonprobability vectors in their arguments share the properties of probabilistic or information theoretic divergences. The results indicate that divergences with nonprobability vectors share, under some conditions, some of the properties of probabilistic or information theoretic divergences and therefore can be considered and used as information measures. We then use these divergences in the problem of actuarial graduation of mortality rates. Copyright © 2009 John Wiley & Sons, Ltd.     

Continuous dependence on parameters of certain self-affine measures,and their singularity

In this paper, we first prove that the self-affine sets depend continuously on the expanding matrix and the digit set, and the corresponding self-affine measures with respect to the probability weight behave in much the same way. Moreover, we obtain some sufficient conditions for certain self-affine measures to be singular.     

A type of Strassen's theorem for positive vector measures with values in dual spaces

In this paper, we extend a type of Strassen's theorem for the existence of probability measures with given marginals to positive vector measures with values in the dual of a barreled locally convex space which has certain order conditions. In this process of the extension we also give some useful properties for vector measures with values in dual spaces.

     

Stability of evolutionary algorithms

We prove under mild conditions the convergence of some evolutionary algorithm to the solution of the global optimization problem. In the proof, the Lyapunov function's techniques is applied to some semi-dynamical system generated by a Foias operator on the space of the probability measures defined on the set of admissible solutions.     

拓扑半群上概率测度的条件组合收敛与SHIFT组合收敛

本文用部分群化的方法,研究拓扑半群上概率测度的条件组合收敛性与SHIFT组合收敛性,得到了一些充分条件,并推广了一些组合收敛性结果.     

Invariant measures for stochastic evolution equations of pure jump type

In this paper, we obtain a characterization of invariant measures of stochastic evolution equations and stochastic partial differential equations of pure jump type. As an application, it is shown that the equation has a unique invariant probability measure under some reasonable conditions.     

Entropy formula and continuity of entropy for piecewise expanding maps

We consider some classes of piecewise expanding maps in finite dimensional spaces having invariant probability measures which are absolutely continuous with respect to Lebesgue measure. We derive an entropy formula for such measures and, using this entropy formula, we present sufficient conditions for the continuity of that entropy with respect to the parameter in some parametrized families of maps. We apply our results to a classical one-dimensional family of tent maps and a family of two-dimensional maps which arises as the limit of return maps when a homoclinic tangency is unfolded by a family of three dimensional diffeomorphisms.     

Artificial Monte Carlo interactions for solving some linear problems

A method of artificial interaction for Monte Carlo solution of ordinary differential equations with respect to probability measures is proposed. Under some conditions this method is advantageous over the standard method. A theoretical comparison of the complexity of the corresponding algorithms is provided. Also, results of numerical experiments are discussed.     

Chordal Loewner families and univalent Cauchy transforms

We study chordal Loewner families in the upper half-plane and show that they have a parametric representation. We show one, that to every chordal Loewner family there corresponds a unique measurable family of probability measures on the real line, and two, that to every measurable family of probability measures on the real line there corresponds a unique chordal Loewner family. In both cases the correspondence is being given by solving the chordal Loewner equation. We use this to show that any probability measure on the real line with finite variance and mean zero has univalent Cauchy transform if and only if it belongs to some chordal Loewner family. If the probability measure has compact support we give two further necessary and sufficient conditions for the univalence of the Cauchy transform, the first in terms of the transfinite diameter of the complement of the image domain of the reciprocal Cauchy transform, and the second in terms of moment inequalities corresponding to the Grunsky inequalities.