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 TITLE: Long range dependent Markov models
SPEAKER: Barlas Oguz (UC Berkeley)
DATE: 2:00 - 3:00 PM, Thursday, May 17, 2012
LOCATION: Tahoe, 3U
ABSTRACT:
We discuss countable state Markov chains as a flexible class of models
for long range dependent sources. We state sufficient conditions under
which an instantaneous function of a long range dependent Markov chain
has the same Hurst index as the underlying chain. We discuss several
applications of the theorem in the fields of information theory,
queuing networks, and finance.
BIOGRAPHY:
Barlas Oguz graduated from Bilkent University in 2007 where he
completed his undergraduate studies in Electrical Engineering. He
went on to continue his studies at the University of California
Berkeley, where he is currently pursuing his PhD. His current
research interests include probability theory and stochastic processes
with applications in information theory and communication networks.
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