Generating Bayesian Network Models from Data Using Tsetlin Machines
This addresses the challenge of constructing interpretable network models for fields like probabilistic reasoning, though it appears incremental as an initial approach.
The paper tackles the problem of building Bayesian network structures from data by proposing an initial methodology using Tsetlin Machines, but no specific results or numbers are provided.
Bayesian networks (BN) are directed acyclic graphical (DAG) models that have been adopted into many fields for their strengths in transparency, interpretability, probabilistic reasoning, and causal modeling. Given a set of data, one hurdle towards using BNs is in building the network graph from the data that properly handles dependencies, whether correlated or causal. In this paper, we propose an initial methodology for discovering network structures using Tsetlin Machines.