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Bayesian networks (BN) are directed acyclic graphical (DAG) models that havebeen adopted into many fields for their strengths in transparency,interpretability, probabilistic reasoning, and causal modeling. Given a set ofdata, one hurdle towards using BNs is in building the network graph from thedata that properly handles dependencies, whether correlated or causal. In thispaper, we propose an initial methodology for discovering network structuresusing Tsetlin Machines.

Generating Bayesian Network Models from Data Using Tsetlin Machines