下临界维数Wiener sausage相交时间的中偏差

本文研究在下临界维数情形下Wiener sausage 的相交时间, 应用新近提出的高阶矩方法和经典的Feynman-Kac 半群方法, 得到该情况下Wiener sausage 相交时间的中偏差.     

平面Wiener Sausage的一个中偏差估计

本文设β(s)表示R~2空间中的布朗运动,|W_r(t)|是由β(s)产生的到时刻t的Wiener sausage.利用Wiener sausage的分解技巧以及一些指数矩估计,得到一个关于|W_r(t)|-E|W_r(t)|的中偏差.     

高维Wiener sausage的强逼近

本文研究了四维及四维以上的Wiener sausage 的体积, 得到它们可以由一维Brown 运动强逼近. 作为应用, 推出了弱收敛和重对数率.     

三维Wiener Sausage体积的中偏差

令{β(s),s≥0}表示R~3空间中的标准Brown运动,|W_r(t)|表示由{β(s),s≥0}产生的观察至时间t且以r为半径的Wiener sausage的体积.由中心极限定理可知,(|W_r(t)|-E|W_r(t)|)/(?)弱收敛至正态分布.本文研究这种情况下的中偏差.     

幂零矩阵群上的Wiener测度

利用Wiener测度与路径积分,Wiener对布朗运动做了完美的分析学描述.通过幂零矩阵群上次拉普拉斯算子的热核,定义了相应的Wiener测度,并且在其上建立了Wiener积分.然后,利用Wiener测度和Wiener积分给出了幂零矩阵群上薛定谔方程的解.     

若干图类的Wiener指数的极值

一个图的Wiener指数是指这个图中所有点对的距离和.Wiener指数在理论化学中有广泛应用. 本文刻画了给定顶点数及特定参数如色数或团数的图中Wiener指数达最小值的图, 同时也刻画了给定顶点数及团数的图中Wiener指数达最大值的图.     

两参数Wiener过程的Cs(o)rg(o)-Révész增量有多大

基于两参数Wiener过程增量的大偏差原理,给出了两参数Wiener过程增量的极限点集.     

两参数 Wiener 过程增量的Strassen 型定理

本文通过建立两参数Wiener过程增量的大偏差结果,在矩形集上研究了两参数Wiener过程的大增量和小增量的Strassen型定理.     

两参数Wiener过程的Csörgő-Révész增量有多大

基于两参数Wiener过程增量的大偏差原理, 给出了两参数Wiener过程增量的极限点集.     

两参数Wiener过程的Csrg"非汉字符号"-Révész增量有多大

基于两参数Wiener过程增量的大偏差原理,给出了两参数Wiener过程增量的极限点集.     

Strong approximations of three-dimensional Wiener sausages

We prove that the centered three-dimensional Wiener sausage can be strongly approximated by a one-dimensional Brownian motion running at a suitable time clock. The strong approximation gives all possible laws of iterated logarithm as well as the convergence in law in terms of process for the normalized Wiener sausage. The proof relies on Le Gall [10]șs fine L ²-norm estimates between the Wiener sausage and the Brownian intersection local times. Research supported by the Hungarian National Foundation for Scientific Research, Grants T 037886, T 043037 and K 61052.     

Rough paths analysis of general Banach space-valued Wiener processes

In this article, we carry out a rough paths analysis for Banach space-valued Wiener processes. We show that most of the features of the classical Wiener process pertain to its rough path analog. To be more precise, the enhanced process has the same scaling properties and it satisfies a Fernique type theorem, a support theorem and a large deviation principle in the same Hölder topologies as the classical Wiener process does. Moreover, the canonical rough paths of finite dimensional approximating Wiener processes converge to the enhanced Wiener process. Finally, a new criterion for the existence of the enhanced Wiener process is provided which is based on compact embeddings. This criterion is particularly handy when analyzing Kunita flows by means of rough paths analysis which is the topic of a forthcoming article.     

THE HENSTOCK-WIENER INTEGRAL

We introduce the Henstook-Wiener integral and prove that it is a generalization of the classical Wiener integral.     

Expected Volume of Intersection of Pinned Wiener Sausages and Heat Kernel Norms on Compact Riemannian Manifolds with Boundary

Estimates are obtained for the expected volume of intersection of independent pinned Wiener sausages in Euclidean space in the limit of small pinning time.      

大偏差与l~p-值Wiener过程在Hlder范数下的泛函连续模

本文在Holder范数生成的强拓扑下,建立了l~2-值Wiener过程的大偏差公式,从而得到了l~2-值与l~p-值Wiener过程在Holder范数下的泛函连续模.     

On Convergence of a P-Algorithm Based on a Statistical Model of Continuously Differentiable Functions

This paper is a study of the one-dimensional global optimization problem for continuously differentiable functions. We propose a variant of the so-called P-algorithm, originally proposed for a Wiener process model of an unknown objective function. The original algorithm has proven to be quite effective for global search, though it is not efficient for the local component of the optimization search if the objective function is smooth near the global minimizer. In this paper we construct a P-algorithm for a stochastic model of continuously differentiable functions, namely the once-integrated Wiener process. This process is continuously differentiable, but nowhere does it have a second derivative. We prove convergence properties of the algorithm.     

How big are the CsörgŐ-Révész increments of two-parameter Wiener processes?

In this paper, we prove a theorem on the set of limit points of the increments of a two-parameter Wiener process via establishing a large deviation principle on the increments of the two-parameter Wiener process.