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07.09.2022 12:15 Marco Scutari (Polo Universitario Lugano, Switzerland):
Bayesian Network Models for Continuous-Time and Structured Data Online: attend (Parkring 11, 85748 Garching)

Bayesian networks (BNs) are a versatile and powerful tool to model complex phenomena and the interplay of their components in a probabilistically principled way. Moving beyond the comparatively simple case of completely observed, static data, which has received the most attention in the literature, I will discuss how BNs can be extended to model continuous data and data in which observations are not independent and identically distributed.

For the former, I will discuss continuous-time BNs. For the latter, I will show how mixed effects models can be integrated with BNs to get the best of both worlds.

14.09.2022 12:15 Michaël Lalancette (University of Toronto, CAN):
t.b.a.Online: attendBC1 2.01.10 (Parkring 11, 85748 Garching)

t.b.a.