平均熵

设Ｔ为紧度量空间Ｘ上的连续自映射,ｍ为Ｘ上的Ｂｏｒｅｌ概率测度,通过把测度（拓扑）摘局部化,引入了Ｔ关于ｍ的平均测度（拓扑）熵的概念,它们分别为相应ｍ－测度（拓扑）混沌吸引子熵的加权平均,从而Ｔ关于ｍ的平均测度（拓扑）熵大于零当且仅当Ｔ有ｍ－测度（拓扑）混沌吸引子．证明了线段Ｉ上关于Ｌｅｂｅｓｇｕｅ测度平均拓扑熵大于Ｃ与等于零的连续自映射都在Ｃ０（Ｉ,Ｉ）中稠密．     

超Ornstein-Uhlenbeck过程的占位时过程的绝对连续性

证明了分支特征为ψ（z)=z^2,底过程为d≤3的暂留Ornstein-Uhlenbeck（O．U．）过程的超过程Xt的占位时过程Y（t)=∫^t0Xsds关于Lebesgue测度绝对连续,且其密度过程Y（t,x)关于t≥0,x∈R^d联合连续。     

超扩散过程的遍历定理

考虑初始测度为Ｌｅｂｅｓｇｕｅ测度μ 的一致椭圆超扩散过程，其分枝特征为ψ（狓，狕）＝犫（狓）狕＋γ（狓）狕２．该文研究这类超过程的占位时过程的极限性质．对系数犫（狓）及γ（狓）做必要的限制，得到了占位时过程在空间维数犱≤２的遍历定理，我们的结果是［６］的补充．     

｛0,1,3｝问题摄动形式所对应的测度

给定实数λ,α以及Ｒ上（以λ,α为参数）的压缩自相似映射Ｓ１（ｘ）＝λｘ, Ｓ２（ｘ）＝ λｘ＋ａ, Ｓ３（ｘ）＝ λｘ＋３,记满足测度方程ｖ＝（１／３）∑ｉ＝１ｖｏＳｉ－１的唯一概率测度为ｕλ,α本文得到：（１）当固定 λ∈Ａ Ｅ（１／３, ２／５）时,则在 Ｌｅｂｅｓｇｕｅ测度意义下,对于 ａ．ｅ．的 ａ∈（０,１）,测度 ｕλ,α绝对连续,且存在平方可积密度．（２）若λ－１是 Ｐ．Ｖ．数,且 α是λ的有理系数多项式,则测度ｕλ,α是奇异测度．     

带迁入超过程的轨道连续性

本文利用鞅测度的方法研究了一类带迁入超过程的轨道连续性问题。给出了这类测度值过程弱连续的一个充要条件，以及在τ－拓扑下连续的充分条件．     

关于测度扩张的若干结论

设Ｘ是任意集合，Ｐ是Ｘ上的集族，空集是Ｐ上的非负广义实值函数且本文讨论了由测度空间（Ｘ．Ｐ，μ）进行外测度扩张所得到的测度空间（Ｘ．Ｓ．μ）．再进行外测度扩张的问题．得到了测度扩张的若干结论。     

半群上的非交换调和分析

设Ａ（Ｍ）是 Ｃ＊－代数（ｖｏｎ Ｎｅｕｍａｎｎ代数）．本文证明了，若φ是定义在Ｒ＋上取值于Ａ（Ｍ）的范数连续的有界正定函数，则φ可表示为Ｒ＋上取值于Ａ＇＇（Ｍ）的有界半变差向量测度的Ｌａｐｌａｃｅ变换．同时也证明了取值于Ａ（Ｍ）的Ｈａｍｂｕｒｇｅｒ型的矩量问题．     

关于可违约债券的一个简化型模型

在Cathcart and E1—Jahel（1998）的Signaling方法的基础上，本文发展了一个具有市场信号变量δt以及随机回收率f（δt）的可违约债券定价的连续时间简化型模型，并用鞅测度的方法给出了近似求解公式．     

稳定分量过程的图集的Packing测度

本文考虑Ｒ~ｄ中具有如下形式的过程：Ｘ（ｔ）＝（Ｘ_１（ｔ），Ｘ_２（ｔ），…，Ｘ_Ｎ（ｔ）），其中Ｘ_ｉ（ｔ）为Ｒ~ｄｉ中指标为α_ｉ的稳定过程（１≤ｉ≤Ｎ），Ｘ_１（ｔ），…，Ｘ_Ｎ（ｔ）相互独立，ｄ＝ｄ１＋…＋ｄ_Ｎ．通过讨论过程Ｇ（ｔ）＝（ｔ，Ｘ（ｔ））的逗留时分布的渐近性质，研究图集Ｇ［０，１］的Ｐａｃｋｉｎｇ测度函数问题。获得了ψ－ｐ（Ｇ［０，１］）＝０或＋∞的积分判别法，或者其确切测度函数．     

Loeb空间中的Radon-Nikodym导数

设υ及μ为定义在可测空间（Ｘ，Ｓ）上的有限测度．本文首先证明了若υ《μ（即υ关于μ绝对连续），则有Ｌ（^*Ｓ，^*μ）(?)Ｌ（＊Ｓ，υ）．进而证明了υ《μ当且仅当Ｌ（^*υ）《Ｌ（^*μ）并且ｄ（Ｌ（^*υ））／ｄ（Ｌ（^*μ））＝^０（^*（ｄυ／ｄμ））即Ｌｏｅｂ空间中的Ｒａｄｏｎ－Ｎｉｋｏｄｙｍ定理．本文按一种自然的方式定义了σ－有限测度空     

On stochastic processes in a multilevel control bulk queueing system

This article analyzes some stochastic processes that arise in a bulk single server queue with continuously operating server, state dependent compound Poisson input flow and general state dependent service process. The authors treat the queueing process as a semi–regenerative process and obtain the invariant probability measure and the transient distribution for the embedded Markov chain. The stationary probability measure for the queueing process with continuous time parameter is found by using semi-regenerative techniques. The authors also study the input and output processes and establish ergodic theorems for some functionals of these processes. The results are obtained in terms of the invariant probability measure for the embedded process and the stationary measure for the queueing process with continuous time parameter     

Palm calculus for a process with a stationary random measure and its applications to fluid queues

We consider a process associated with a stationary random measure, which may have infinitely many jumps in a finite interval. Such a process is a generalization of a process with a stationary embedded point process, and is applicable to fluid queues. Here, fluid queue means that customers are modeled as a continuous flow. Such models naturally arise in the study of high speed digital communication networks. We first derive the rate conservation law (RCL) for them, and then introduce a process indexed by the level of the accumulated input. This indexed process can be viewed as a continuous version of a customer characteristic of an ordinary queue, e.g., of the sojourn time. It is shown that the indexed process is stationary under a certain kind of Palm probability measure, called detailed Palm. By using this result, we consider the sojourn time processes in fluid queues. We derive the continuous version of Little's formula in our framework. We give a distributional relationship between the buffer content and the sojourn time in a fluid queue with a constant release rate.     

The critical measure diffusion process

Summary A multiplicative stochastic measure diffusion process is the continuous analogue of an infinite particle branching diffusion process. In this paper the limiting behavior of the critical measure diffusion process is investigated. Conditions are found under which a non-trivial steady state random measure exists and in this case a spatial central limit theorem is established.Supported in part by the National Research Council of Canada.     

Diffusions of multiplicative cascades

A multiplicative cascade can be thought of as a randomization of a measure on the boundary of a tree, constructed from an iid collection of random variables attached to the tree vertices. Given an initial measure with certain regularity properties, we construct a continuous time, measure-valued process whose value at each time is a cascade of the initial one. We do this by replacing the random variables on the vertices with independent increment processes satisfying certain moment assumptions. Our process has a Markov property: at any given time it is a cascade of the process at any earlier time by random variables that are independent of the past. It has the further advantage of being a martingale and, under certain extra conditions, it is also continuous. For Gaussian independent increment processes we develop the infinite-dimensional stochastic calculus that describes the evolution of the measure process, and use it to compute the optimal Hölder exponent in the Wasserstein distance on measures. We also discuss applications of this process to the model of tree polymers.     

A note on the mean correcting martingale measure for geometric Lévy processes

A martingale measure is constructed by using a mean correcting transform for the geometric Lévy processes model. It is shown that this measure is the mean correcting martingale measure if and only if, in the Lévy process, there exists a continuous Gaussian part. Although this measure cannot be equivalent to a physical probability for a pure jump Lévy process, we show that a European call option price under this measure is still arbitrage free.     

Bayesian nonparametric statistical inference for Poisson point processes

Summary A random measure is said to be selected by a weighted gamma prior probability if the values it assigns to disjoint sets are independent gamma random variables with positive multipliers. If the intensity measure of a nonhomogeneous Poisson point process is selected by a weighted gamma prior probability and if a sample is drawn from the Poisson point process having this intensity measure, then the posterior random intensity measure given the observations is also selected by a weighted gamma prior probability. If the measure space is Euclidean and if the true intensity measure is continuous and finite, the centered posterior process, rescaled by the square root of the sample size, will converge weakly in Skorohod topology to a Wiener process subject to a change of time scale.This research was supported in part by the National Science Foundation Grants MCS 77-10376 and MCS 75-14194     

Absolutely continuous measure for a jump-type Fleming-Viot process

In this paper, we prove that the random measure of the one-dimensional jump-type Fleming-Viot process is absolutely continuous with respect to the Lebesgue measure in R, provided the mutation operator satisfies certain regularity conditions. This result is an important step towards the representation of the Fleming-Viot process with jumps in terms of the solution of a stochastic partial differential equation.     

Existence of a σ finite invariant measure for a Markov process on a locally compact space

A σ finite invariant measure is found, for a Markov process, on a locally compact space, which maps continuous functions to continuous functions. The research reported in this document has been sponsored by the Air Force Office of Scientific Research under Grant AF EOAR 66-18, through the European Office of Aerospace Research (OAR) United States Air Force.     

Hamiltonian systems with a stochastic force:nonlinear versus linear,and a girsanov formula

We discuss stochastic perturbations of classical Hamiltonian systems by a white noise force. We prove existence and uniqueness results for the solutions of the equation of motion under general conditions on the classical system, as well as their continuous dependence on the initial conditions. We also prove that the process in phase space is a diffusion with transition probability densities, and Lebesgue measure as c-finite invariant measure. We prove a Girsanov formula relating the solution for a nonlinear force with the one for a linear force, and give asymptotic estimates on functions of the phase space process     

On characterisation of Markov processes via martingale problems

It is well-known that well-posedness of a martingale problem in the class of continuous (or r.c.l.l.) solutions enables one to construct the associated transition probability functions. We extend this result to the case when the martingale problem is well-posed in the class of solutions which are continuous in probability. This extension is used to improve on a criterion for a probability measure to be invariant for the semigroup associated with the Markov process. We also give examples of martingale problems that are well-posed in the class of solutions which are continuous in probability but for which no r.c.l.l. solution exists.