Comparative Analysis of Probabilistic Programming Languages in Python (2025) Theory of Neural Systems

Description

The goal of this topic is a comparative analysis of several probabilistic programming languages (PPLs) available in Python, namely Tensorflow Probability, Pyro, Numpyro and PyMC.

It should consider both,  qualitative (available algorithms, how active is the community, completeness of documentation, what are community recommendation, ...) as well as a quantitative aspects (empirical time and memory complexity, scalability, utilization of specialized hardware) for selected reference models.

Please note: The topic very ambitious, not all languages / reference models / algorithms need to be evaluated for a good result!

Requirements:

  • Python experience
  • Basic probability theory
  • General understanding of deep learning

If you are interested in this topic or a variation of it, feel free to contact me!

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

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