Wednesday, May 6, 2020 - 4:00pm to 4:30pm
Event Calendar Category
LIDS & Stats Tea
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Zoom meeting id
581 168 821
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Rajat will discuss a new framework of uncertainty variables to model uncertainty. An uncertainty variable is characterized by a set in which its realization is bound to lie. Conditional uncertainty, on the other hand, is characterized by a set-map, from a given realization of one variable to a set of possible realizations of another. We prove Bayes’ law and the law of total probability equivalents for uncertainty variables. We extend the notion of independence, conditional independence, and pairwise independence for a collection of uncertainty variables, and show that this new notion of independence preserves the properties of independence as we know them for random variables. We also develop graphical models over a collection of uncertainty variables and show that the expected conditional independence properties continue to hold true.
Reference: Rajat Talak, Sertac Karaman, and Eytan Modiano "A Theory of Uncertainty Variables for State Estimation and Inference" Allerton 2019.
Rajat Talak is a Ph.D. student in the Laboratory of Information and Decision Systems at Massachusetts Institute of Technology. He works with Prof. Eytan Modiano and Prof. Sertac Karaman. His research interests are in the field of multi-agent systems, communication networks, machine learning, and autonomy. His recent work also includes designing of communication algorithms with provable guarantees on information freshness. For his work on optimizing information freshness in wireless networks, he was awarded the MobiHoc 2018 Best Paper Award.