Publications

Reactive Probabilistic Programming for Scalable Bayesian Inference

Dmitry V. Bagaev • Eindhoven University of Technology

2023

Bayesian inference realizes optimal information processing through a full commitment to reasoning by probability theory. The Bayesian framework is a crucial technology at the core of modern AI with applications such as speech and image recognition and generation, biomedical analysis, robot navigation, and more. This dissertation focuses on the realization of efficient Bayesian inference in large-scale probabilistic models, targeting real-time signal processing and control applications under real-world conditions. We present a practical architecture based on reactive message passing-based inference in a factor graph representation of the probabilistic model under study.