Monte Carlo sampling & hitting times (Dr. Andrea Lecchini-Visintini, University of Leicester)

I will start by introducing some results on the use of independent Monte Carlo sampling for probabilistic performance analysis. In particular, I will recall that the empirical optimum constructed from independent samples is an efficient estimator of probabilistic performance with rigorous finite-sample guarantees. I will then present some work in progress towards obtaining similar finite-sample results when samples are generated from a single run of a Markov chain. Although this work is motivated by the analysis of probabilistic performance, it is related to the more general problem of characterising the hitting time probabilities of a Markov chain.

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May 04, 2017
from 02:00 PM to 03:00 PM


MAB 119

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