TU Wien Informatics

Probabilistic Programming: Machine Learning for the Masses?

  • 2019-12-04

Joost-Pieter Katoen (RWTH Aachen) gives a historical perspective and indicates what formal methods can mean for probabilistic programs.

Probabilistic Programming: Machine Learning for the Masses?

Probabilistic programming is a fascinating new direction in programming. FaceBook, Google and Microsoft, to mention a few, are investing lots of research efforts in probabilistic programming. Nearly every programming language has a probabilistic version. Scala, JavaScript, Haskell, Prolog, C, Python, you name it, and – yes – even Excel has been extended with features for randomness. These languages aim to make probabilistic modeling and machine learning accessible to any programmer, any user.

Probabilistic programs describe recipes on how to infer conclusions about big data from a mixture of uncertain data and real-world observations. Bayesian networks, a key model in decision making, are simple instances of such programs. Probabilistic programs steer autonomous robots and self-driving cars, are key to describe security mechanisms, naturally encode randomised algorithms, and are rapidly encroaching AI and machine learning.

About Joost-Pieter Katoen

Joost-Pieter Katoen is a full professor at RWTH Aachen University in the Software Modeling and Verification (MOVES) group and part-time associated to the Formal Methods & Tools group at the University of Twente. Since 2013, he holds a distinguished professorship at RWTH Aachen University and is a member of the Academia Europaea.

Curious about our other news? Subscribe to our news feed, calendar, or newsletter, or follow us on social media.