Selfsimilar stochastic processes (p... (展开全部) 丛书信息 ··· Princeton Series in Applied Mathematics(共34册),这套丛书还有 《Optimization》《Statistical Inference via Convex Optimization》《Graph Theoretic Methods in Multiagent Networks》《Self-regularity》《Matrices, Moments and Quadrature with Ap...
网络自相似随机过程 网络释义 1. 自相似随机过程 ..., long memory processes)、自相似随机过程(self-similar processes)、分形随机过程(fractal random processes);2) 谱分析(sp… www.freewebs.com|基于2个网页 例句
second-order self-similar processes：二阶自相似过程 二阶自相似： asymptotically second order self-similar ；例句：但对于一般形式，目前，只知道其二阶结构（自协方差和谱）与线性ARMA模型相似。According to the second order structure (i. e. , auto-variance and spectral density), it is si...
英文短句/例句 1.Ergodic in Self-similar Processes and Properties of Related Function;自相似过程的遍历性及相关函数的性质 2.Modeling and Performance Analysis of Network Traffic Based on α-Stable Self-Similar Process;基于α-稳定自相似过程的网络业务建模与性能分析 3.The Self-Similar Stochastic Progress ...
It examines the operations and functions of self-similar processes. It was suggested that the program has the advantage of being applicable to stationary processes with or without finite second moment. Moreover, the boundary between short and long memory is, further, given by a single number, a...
: Mimicking self-similar processes. Preprint, 2012.Fan, J.Y. Hamza, K., Klebaner, F.C. : Mimicking self-similar processes. Bernoulli, 21(3):1341-1360, 2015.J. Y. Fan, K. Hamza, and F. Klebaner. Mimicking self-similar processes. Bernoulli, 21(3):1341-1360, 08 2015....
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A class of second-order stationary self-similar processes for 1/f phenomena We introduce a class of stochastic processes whose correlation function obeys a structure of the form, E[X(t)X(/spl lambda/t)]=t/sup 2H//spl lambda//sup H/... B Yazici,RL Kashyap - 《Signal Processing IEEE ...
Laws of the iterated logarithm of "limsup" type are studied formulti-dimensional selfsimilar processes $\\\{X(t)\\\}$ with independent incrementshaving exponent $H$. It is proved that, for any positive increasing function$g(t)$ with $\\\displaystyle \\\lim_{to\\\infty}g(t) = \\...