Talk Abstract
Automatic transformation of Bayesian probabilistic models into interactive visualisations: models expressed in a probabilistic programming language are translated automatically into interactive multiverse diagrams, a graphical representation of the model’s structure at varying levels of granularity, with seamless integration of uncertainty visualisation. A concrete implementation in Python that translates probabilistic programs to interactive multiverse diagrams will be presented and illustrated by examples for a variety of Bayesian probabilistic models.
| Evdoxia Taka | Twitter @evdoxiataka |
GitHub evdoxiataka |
Talk
Evdoxia Taka
Evdoxia is a PhD student at the School of Computing Science of University of Glasgow since 2019. Her research focuses on the creation of novel representations of probabilistic models that incorporate animation and interaction for a more intuitive communication of the uncertainty in the variables of probabilistic models. She became a Python and Bayesian enthusiast ever since she started her PhD and she got a foot in the door of a whole new-to-her, but very charming world. Evdoxia completed her undergraduate and master studies in the Aristotle University of Thessaloniki, Greece as Electrical and Computer Engineer. She worked as a Research Assistant at the Centre for Research & Technology Hellas in Thessaloniki contributing to various national- and EU-funded research projects in areas such as computer vision, 3D reconstruction and simulation, machine learning. She has also worked as a Research Database Engineer for the HCV Research UK project at the Centre for Virus Research of the University of Glasgow.
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