Joost-Pieter Katoen demystifies probabilistic modeling

Joost-Pieter Katoen, Dawid Kasprowicz, Stefan Böschen; Joost-Pieter Katoen cites Ghahramani (2015: 455): “There are several reasons why probabilistic programming could prove to be revolutionary for machine learning and scientific modelling.”

On July 13th, Professor Joost-Pieter Katoen (RWTH Aachen University) gave the final Philosophy of AI lecture: Optimistic and Pessimistic Views lecture at c:o/re, titled “Demystifying probabilistic programming“. The talk convincingly advocated the usefulness and accuracy of probabilistic inferences as performed by computers. Various types of machine learning, argued Joost-Pieter Katoen, can benefit from being developed through probabilistic programming. The underling claim is that probabilistic programs are a universal modeling formalism. Far from implying that this could result in softwares that could successfully replace humans from inferential and decision-making processes, probabilistic programming relies on correct parameterisation, which is an input provided by humans.

The c:o/re team would like to thank Professor Frederik Stjernfelt and Dr. Markus Pantsar for organizing the lecture series Philosophy of AI: Optimistic and Pessimistic Views, which ran throughout the summer semester of 2022.


probabilistic inference

Training of neural networks


Ghahramani, Zoubin. 2015. Probabilistic machine learning and artificial intelligence. Nature 521: 452–459.

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