Julia programmers, probabilistic modelers, and Active Inference practitioners working with RxInfer.jl.
Learning, applying, and extending the RxInfer.jl reactive message-passing inference system.
Lead: Daniel Friedman
The RxInfer.jl Learning and Development Group supports learning and extending RxInfer.jl, a Julia package for reactive Bayesian inference. The group produces learning resources, code examples, and contributes to the broader development of RxInfer as a platform for Active Inference.
RxInfer.jl is a reactive message-passing probabilistic programming system developed externally and adopted by the Institute as a key platform for Active Inference implementations. The Institute's learning group focuses on understanding, applying, and extending RxInfer across Active Inference use cases — building examples, documentation, and learning pathways for community members.
The group maintains a project overview, learning sessions, and code in the Institute repository. Active sessions appear in the Institute's activities calendar.
Participants can join learning sessions, contribute examples, or help improve RxInfer's visualization capabilities. Julia programming experience is helpful but structured learning sessions accommodate all levels.
Related resources
Audience: Newcomer
Primary public community channel for conversation, coordination, and newcomer orientation.
Official pages
Audience: Contributor
Public activities shortlink, currently routed to the Institute's Projects directory of repositories, research, and applied work.
Audience: Developer
Public RxInfer learning-group shortlink for implementation-oriented Active Inference work.
Repositories
Audience: Learner
Active Inference and RxInfer.jl