On Hermitian matrices diffusion and Dyson Brownian motion with general potential

来源 :上海交通大学 | 被引量 : 0次 | 上传用户:luxiliang
下载到本地 , 更方便阅读
声明 : 本文档内容版权归属内容提供方 , 如果您对本文有版权争议 , 可与客服联系进行内容授权或下架
论文部分内容阅读
  We consider diffusion processes on Hermitian matrices ensemble and the corresponding eigenvalues processes,the so-called Dyson Brownian motion.
其他文献
Thousands of genome-wide association studies(GWAS)have been conducted over the past decade to identify the genetic risk variants underlying such complex phenotypes as human height,diabetes,and psychia
This paper estimates the volatility of volatility based on noisy high frequency data.We use the pre-averaging method to build an estimator for spot volatility,based on which we construct a consistent
Length-biased and right-censored failure time data arise from many fields and their analysis has recently attracted a great deal of attention(Huang and Qin,2012; Qin and Shen,2010; Tsai,2009; Vardi,19
In many applications,the quality of products or services tends to be measured by multiple categorical characteristics,each of which is classified into attribute levels such as good,marginal,and bad.He
This paper presents a new remaining useful life prediction method for deteriorating systems subjected to both continuous smooth degradation and additional abrupt damages due to a shock process,which c
会议
In many applications,data are stored in the form of matrices.Various tools for di-mension reduction are available by approximating a matrix using the product of some low-rank matrices.
This paper introduces a unifued model,which can accommodate both a continuous-time It(o) process used to model high-frequency stock prices and a GARCH process employed to model low-frequency stock pri
Thousands of online lending platforms have emerged since 2011,solving financial difficulties of small and medium-size enterprises to some extent but also followed by various problems,e.g.platform absc
By using advanced motion capture systems,human movement data can be collected densely over time and functional data analysis provides useful tools for analysing the resulting curves.
It is well known that it is important to control the bias in esti-mating conditional expectations in order to obtain asymptotic normality for quantities of interest(e.g.a finite dimensional parameter