The INI has a new website!

This is a legacy webpage. Please visit the new site to ensure you are seeing up to date information.

Stochastic Networks

22 March - 26 March 2010

Isaac Newton Institute for Mathematical Sciences, Cambridge, UK

Workshop Organisers: Takis Konstantopoulos (Heriot-Watt) and Kavita Ramanan (Brown University).

Workshop Programme Committee: Vivek Borkar (Tata Institute of Fundamental Research), Serguei Foss (Heriot-Watt University), Peter Glynn (Stanford),

Bruce Hajek (University of Illinois), Frank Kelly (Cambridge), P.R. Kumar (University of Illinois), Tom Kurtz (University of Wisconsin-Madison), Jean Mairesse (LIAFA), Philippe Robert (INRIA), John Tsitsiklis (MIT) and Ruth Williams (University of California, San Diego)

in association with the Newton Institute programme

Stochastic Processes in Communication Sciences (11 January to 2 July 2010)

Participants | Application | Accommodation and Cost

Professor Venkat Anantharam:

Title: Persistence of long-range-dependence under data compression

Affiliation: EECS Department University of California Berkeley, CA 94720 (joint work with Barlas Oguz)

One of the early motivations for current interest in the stochastic networks community in the study of network models involving long-range-dependent stochastic processes was the observation, based on statistical analysis of data, that variable-bit-rate video traffic over networks appears to exhibit long-range-dependent behavior. Such traffic is typically placed on the network after data compression algorithms are used on an underlying video source. It is natural to ask what role the data compression algorithm plays in the resulting long-range-dependent nature of the traffic. Motivated by this question we study the entropy density of an underlying long-range-dependent process as a stochastic process in its own right, focusing on discrete time models. For classes of processes including renewal processes we prove that long-range-dependence of the underlying process implies long-range-dependence of the entropy density process, with the same Hurst exponent. The underlying background in the data compression of stochastic processes, including the fundamental lemma of Barron relating the entropy density to data compression, and existing results for the short-range-dependent case that have the same flavor as our results, such as those due to Kontoyiannis, will also be discussed in this talk.

Local Information | Newton Institute Map | Stochastic Processes in Communication Sciences | Workshops | Newton Institute Home Page

Copyright © Isaac Newton Institute