PEPC: Parallel and Extensible PC Implementation for Causal Structure Learning-Reference-Cited by-同舟云学术

PEPC: Parallel and Extensible PC Implementation for Causal Structure Learning

Published:2023-06-17 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2023 7th International Conference on High Performance Compilation, Computing and Communications
language:
Short-container-title:

Author:

Feng Yongquan¹^ORCID,Xu Liyang¹^ORCID,Huang Wanrong¹^ORCID,Yang Wenjing¹^ORCID,Lu Yutong²^ORCID

Affiliation:

1. Institute for Quantum Information State Key Laboratory of High Performance Computing, College of Computer Science and Technology, National University of Defense Technology, China

2. Institute for Quantum Information State Key Laboratory of High Performance Computing, College of Computer Science and Technology, National University of Defense Technology, China and School of Computer Science and Engineering, Sun Yat-sen University, China

Funder

National Natural Science Foundation of China

National University of Defense Technology

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3606043.3606054

Reference25 articles.

1. David Maxwell Chickering . 1996. Learning Bayesian networks is NP-complete . In Learning from data . Springer , 121–130. David Maxwell Chickering. 1996. Learning Bayesian networks is NP-complete. In Learning from data. Springer, 121–130.

2. Diego Colombo , Marloes H Maathuis , Markus Kalisch , and Thomas S Richardson . 2012. Learning high-dimensional directed acyclic graphs with latent and selection variables. The Annals of Statistics ( 2012 ), 294–321. Diego Colombo, Marloes H Maathuis, Markus Kalisch, and Thomas S Richardson. 2012. Learning high-dimensional directed acyclic graphs with latent and selection variables. The Annals of Statistics (2012), 294–321.

3. The QR Transformation--Part 2

4. Using Bayesian Networks to Analyze Expression Data