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This paper proposes Neural-Bayesian Structure Learning, a framework that integrates differentiable structure learning with discrete choice modeling to predict choice behavior and evaluate interventions, achieving comparable performance while recovering coherent dependency structures.
Introduces Neural Bayesian Sequential Routing (NBSR), a framework that models neural inference as sequential evidence accumulation over a DAG using Dirichlet-Categorical conjugate updates, enabling uncertainty quantification, early exiting, and resource-rational inference.