ForneyLab.jl: a Julia Toolbox for Factor Graph-based Probabilistic Programming

Abstract

Scientific modeling concerns a continual search for better models for given data sets. This process can be elegantly captured in a Bayesian inference framework. ForneyLab enables largely automated scientific design loops by deriving fast, analytic algorithms for approximate Bayesian inference.

Publication
Julia Conference
Thijs van de Laar
Thijs van de Laar
Assistant professor

I am an assisant professor at BIASlab, where I work on artificial agents that learn to control themselves in uncertain environments. I take inspiration from physics and neuroscience, and develop theory and (software) tools that allow for efficient, real-time interaction.

Marco Cox
Marco Cox
Former PhD student

Former researcher at BIASlab.

Bert de Vries
Bert de Vries
Professor

I am a professor at TU Eindhoven and team leader of BIASlab.