Over the last few decades, Bayesian Networks (BNs) have become an increasingly popular AI approach. In this workshop we explore methodologies for enhancing the effectiveness of BNs including modeling, reasoning, model selection, logic-probability relations, and causality. The exploration of methodologies is complemented discussions of practical considerations for applying BNs in real world settings, covering concerns like scalability, incremental learning, parallelization, and so on.

We welcome contributions related to the following topics, but are not limited to:
- Probabilistic Reasoning using Bayesian Networks
- Combination of Probabilistic Reasoning and Logic in Bayesian Networks
- Learning Bayesian Networks
- Causal Discovery
- Causal Inference
- Advanced Application of Bayesian Networks