Suppr超能文献

基于马尔可夫链的长程相关性生成方法。

Markov chain-based method for generating long-range dependence.

作者信息

Clegg Richard G, Dodson Maurice

机构信息

Department of Mathematics, University of York, York YO10 5DD, United Kingdom.

出版信息

Phys Rev E Stat Nonlin Soft Matter Phys. 2005 Aug;72(2 Pt 2):026118. doi: 10.1103/PhysRevE.72.026118. Epub 2005 Aug 16.

Abstract

This paper describes a model for generating time series which exhibit the statistical phenomenon known as long-range dependence (LRD). A Markov modulated process based on an infinite Markov chain is described. The work described is motivated by applications in telecommunications where LRD is a known property of time series measured on the Internet. The process can generate a time series exhibiting LRD with known parameters and is particularly suitable for modeling Internet traffic because the time series is in terms of ones and zeros, which can be interpreted as data packets and interpacket gaps. The method is extremely simple, both computationally and analytically, and could prove more tractable than other methods described in the literature.

摘要

本文描述了一种用于生成时间序列的模型,该时间序列呈现出被称为长程相依(LRD)的统计现象。描述了一种基于无限马尔可夫链的马尔可夫调制过程。所描述的工作是受电信领域应用的推动,在电信领域中,LRD是在互联网上测量的时间序列的一个已知特性。该过程可以生成具有已知参数的呈现LRD的时间序列,并且特别适合于对互联网流量进行建模,因为该时间序列是由1和0组成的,这可以解释为数据包和包间间隙。该方法在计算和分析方面都极其简单,并且可能比文献中描述的其他方法更易于处理。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验