Generating Bayesian Network Models from Data Using Tsetlin Machines
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Abstract (arXiv)
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.
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References · 5
- 2018The Tsetlin Machine -- A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logiccited by 72
- 2021Coalesced Multi-Output Tsetlin Machines with Clause Sharingcited by 22
- 2014Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimised Implementations in the bnlearn R Packagecited by 19
- 2020Extending the Tsetlin Machine With Integer-Weighted Clauses for Increased Interpretabilitycited by 16
- 2021Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin Machinecited by 7
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