Seminar: Probabilistic Independence, Graphs, and Random Networks

SpeakerKayvan Sadeghi
AffiliationUCL
DateFriday, 16 Nov 2018
Time13:00 - 14:00
LocationRoberts G08
Event seriesDeepMind CSML Seminar Series
Description

The main purpose of this talk is to explore the relationship between the set of conditional independence statements induced by a probability distribution and the set of separations induced by graphs as studied in graphical models. I introduce the concepts of Markov property and faithfulness, and provide conditions under which a given probability distribution is Markov or faithful to a graph in a general setting. I discuss the implications of these conditions in devising structural learning algorithms, in understanding exchangeable vectors, and in random network analysis.

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