KG-SoftMAP: Soft Knowledge-Graph Priors for Bayesian Network Structure Learning from Sparse Discrete Data

KG-SoftMAP is a new method for learning Bayesian network structures from sparse discrete data by incorporating soft knowledge graph (KG) priors into the process. The approach utilizes a confidence-weighted, data-overridable edge prior to maximize a MAP objective, combining the BDeu score with a logit-form prior, and demonstrates improved performance on synthetic benchmarks, achieving a DF1 score increase from 0.14 to 0.96 with informative KGs. This method is significant for practitioners as it allows for the integration of imperfect domain knowledge, enhancing structure recovery and inference capabilities in scenarios where data is limited.