Contraction Estimates for Markov Kernels via Information-Transportation Inequalities

Thursday, April 16, 2015 - 2:50pm - 3:40pm
Keller 3-180
Maxim Raginsky (University of Illinois at Urbana-Champaign)
This talk deals with quantifying the rate of contraction of the relative entropy with respect to a reference probability measure under the action of a Markov kernel. Sharp contraction estimates for Markov kernels are useful in a variety of settings, including distributed communication and simulation, exact and approximate stochastic filtering, analysis of MCMC algorithms, and statistical physics. I will present a method for obtaining such estimates that relies on information-transportation inequalities, which were introduced by Katalin Marton in a series of breakthrough papers. I will illustrate this method through examples of random walks on graphs and information flow between disjoint subsets of spins in probabilistic graphical models.
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