You are invited to attend a lecture by
Prof. Yarim Ephraim
Department of ECE, George Mason University
Title: Parameter Estimation for Bivariate Markov Chains
Date: Wednesday, 6/7/2011
Time: at 13:30
Place: in room 1066
Electrical Eng. Building
The original announcement for this is now at
It will be available for up to a month after the date of the activity.
Abstract: Bivariate Markov chains are used in many applications such as
communication network modeling, ion-channel currents estimation, and
phylogenetics analysis. Only one process of the bivariate Markov chain
is observable, while the other plays the role of an underlying process.
Bivariate Markov chains are Markov renewal processes, and they may also
be seen as hidden Markov processes. Some well-known examples of
bivariate Markov chains include the Markov modulated Poisson process and
the batch Markovian arrival process. In these examples, the generator of
the bivariate Markov chain is such that the underlying process is a
Markov chain. Moreover, in the first example, the underlying and
observable processes cannot jump simultaneously. In this talk, we
present some of our recent results on signal and parameter estimation of
a finite-state homogeneous general bivariate Markov chain for which the
underlying process need not be Markov and the two processes may jump
simultaneously. We have developed explicit recursions for some of these
problems which do not require any transformation or sampling scheme of
the observed signal.
. This is joint work with Brian L. Mark.
. The work was supported by the U.S. National Science Foundation under Grant 0916568.
Technion Math. Net (TECHMATH)
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