Entropy of Hidden Markov Processes and Connections to Dynamical Systems (07w5103)


(University of British Columbia)

(University of North Carolina)

Tsachy Weissman (Stanford University)


Hidden Markov Processes (HMP's) are models of a variety of phenomena observed in the presence of noise. These range from speech and optical character recognition, through target tracking, to biomolecular sequence analysis. These processes are also of great interest in error control coding for noisy communication channels and data recording systems. One fundamental problem is computation of the entropy of an HMP. The entropy is a measure of randomness or complexity of the process and expresses the
degree to which the process can be compressed without losing information. While there is no known formula for the exact value of the entropy, there are good approximations.
During the week of October 1, a group of thirty-five experts from universities and industry will meet at the Banff International Research Station (BIRS) to exchange ideas on recent progress and to develop innovative approaches to solving the fundamental problems. Participants will include mathematicians, electrical engineers and physicists from several countries in North America, Europe and Asia.

The Banff International Research Station for Mathematical Innovation and Discovery (BIRS) is a collaborative Canada-US-Mexico venture that provides an environment for creative interaction as well as the exchange of ideas, knowledge, and methods within the Mathematical Sciences, with related disciplines and with industry. The research station is located at The Banff Centre in Alberta and is supported by Canada's Natural Science and Engineering Research Council (NSERC), the US National Science Foundation (NSF), Alberta's Advanced Education and Technology, and Mexico's Consejo Nacional de Ciencia y Tecnología (CONACYT).