The Annals of Statistics
- Ann. Statist.
- Volume 46, Number 6A (2018), 3151-3183.
High-dimensional consistency in score-based and hybrid structure learning
Main approaches for learning Bayesian networks can be classified as constraint-based, score-based or hybrid methods. Although high-dimensional consistency results are available for constraint-based methods like the PC algorithm, such results have not been proved for score-based or hybrid methods, and most of the hybrid methods have not even shown to be consistent in the classical setting where the number of variables remains fixed and the sample size tends to infinity. In this paper, we show that consistency of hybrid methods based on greedy equivalence search (GES) can be achieved in the classical setting with adaptive restrictions on the search space that depend on the current state of the algorithm. Moreover, we prove consistency of GES and adaptively restricted GES (ARGES) in several sparse high-dimensional settings. ARGES scales well to sparse graphs with thousands of variables and our simulation study indicates that both GES and ARGES generally outperform the PC algorithm.
Ann. Statist., Volume 46, Number 6A (2018), 3151-3183.
Received: May 2016
Revised: June 2017
First available in Project Euclid: 7 September 2018
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Zentralblatt MATH identifier
Bayesian network directed acyclic graph (DAG) linear structural equation model (linear SEM) structure learning greedy equivalence search (GES) score-based method hybrid method high-dimensional data consistency
Nandy, Preetam; Hauser, Alain; Maathuis, Marloes H. High-dimensional consistency in score-based and hybrid structure learning. Ann. Statist. 46 (2018), no. 6A, 3151--3183. doi:10.1214/17-AOS1654. https://projecteuclid.org/euclid.aos/1536307246
- Supplement to “High-dimensional consistency in score-based and hybrid structure learning”. All proofs, additional simulation results, additional details for Example 1 can be found in the supplementary material.