The More, the Merrier: The Power of Relations for Probabilistic Graphical Models

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Abstract

Our day-to-day life is characterised by uncertainty that we as humans implicitly deal with. Probabilistic graphical models like Bayesian networks or Markov networks and factor graphs explicitly model these uncertain processes to allow for probabilistic inference. These models however do not have a notion of individuals and relations among them. And our world exists of things related to things like humans connected to humans through family, work, or friendship. Combining probabilistic models with a notion of relations opens up a whole new world of possibilities: on a technical level, it allows for tractable probabilistic inference. Beyond that, it provides exciting new inference problems like asking for the most probable source of an observation and enables to reach meta-goals such as privacy-preserving probabilistic inference. This talk covers how relations help solve known and new problems in probabilistic inference.

Speakers from the University of Münster

Braun, Tanya

Junior professorship for practical computer science - modern aspects of data processing / data science (Prof. Braun)