Semi-parametric Expert Bayesian Network Learning with Gaussian Processes and Horseshoe Priors

Abstract

This paper proposes a model learning Semi-parametric relationships in an Expert Bayesian Network (SEBN) with linear parameter and structure constraints. We use Gaussian Processes and a Horseshoe prior to introduce minimal nonlinear components. To prioritize modifying the expert graph over adding new edges, we optimize differential Horseshoe scales. In real-world datasets with unknown truth, we generate diverse graphs to accommodate user input, addressing identifiability issues and enhancing interpretability. Evaluation on synthetic and UCI Liver Disorders datasets, using metrics like structural Hamming Distance and test likelihood, demonstrates our models outperform state-of-the-art semi-parametric Bayesian Network model.