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FROSTY: A High-Dimensional Scale-Free Bayesian Network Learning Method
Volume 21, Issue 2 (2023): Special Issue: Symposium Data Science and Statistics 2022, pp. 354–367
Pub. online: 14 March 2023      Type: Statistical Data Science      Open accessOpen Access
Received
31 July 2022
Accepted
7 March 2023
Published
14 March 2023

Abstract

We propose a scalable Bayesian network learning algorithm based on sparse Cholesky decomposition. Our approach only requires observational data and user-specified confidence level as inputs and can estimate networks with thousands of variables. The computational complexity of the proposed method is $O({p^{3}})$ for a graph with p vertices. Extensive numerical experiments illustrate the usefulness of our method with promising results. In simulation, the initial step in our approach also improves an alternative Bayesian network structure estimation method that uses an undirected graph as an input.

Supplementary material

 Supplementary Material
Supplementary material includes proofs of the theorems and the Python code for simulation.

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2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
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Keywords
Bayesian networks robust selection sparse Cholesky decomposition structure learning

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