Speaker |
Kayvan Sadeghi |
|---|---|
Affiliation |
UCL |
Date |
Friday, 16 November 2018 |
Time |
13:00-14:00 |
Location |
Zoom |
Link |
Roberts G08 |
Event series |
DeepMind/ELLIS CSML Seminar Series |
Abstract |
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. |
Biography |