The 13th International Conference on Probabilistic Graphical Models

The 13th International Conference on Probabilistic Graphical Models (PGM 2026) is a long-running biennial conference dedicated to the theory and applications of probabilistic graphical models, a foundational framework for reasoning under uncertainty in artificial intelligence. PGM brings together researchers who use graphs to represent statistical dependencies and perform principled inference.

Topics include Bayesian networks, Markov random fields, causal inference, approximate inference algorithms, structure learning, deep generative models, and applications in computational biology, natural language processing, robotics, and medicine. The conference attracts machine learning theorists, statisticians, and applied AI researchers who value mathematical rigor in probabilistic modeling. Graduate students working on uncertainty quantification, generative modeling, or causal reasoning will find PGM an intellectually stimulating and community-oriented venue.