A million variables and more: the Fast Greedy Equivalence Search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images-Reference-Cited by-同舟云学术

A million variables and more: the Fast Greedy Equivalence Search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images

Published:2016-12-01 Issue:2 Volume:3 Page:121-129
ISSN:2364-415X
Container-title:International Journal of Data Science and Analytics
language:en
Short-container-title:Int J Data Sci Anal

Author:

Ramsey Joseph,Glymour Madelyn,Sanchez-Romero Ruben,Glymour Clark

Funder

National Institutes of Health

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computational Theory and Mathematics,Computer Science Applications,Modelling and Simulation,Information Systems

Link

http://link.springer.com/article/10.1007/s41060-016-0032-z/fulltext.html

Reference16 articles.

1. Hastie, T., Tibshirani, R., Friedman, J., Franklin, J.: The elements of statistical learning: data mining, inference and prediction. Math. Intell. 27(2), 83–85 (2005)

2. Friedman, J., Hastie, T., Tibshirani, R.: Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9(3), 432–441 (2008)

3. Schwarz, G.: Estimating the dimension of a model. Ann. Stat. 6(2), 461–464 (1978)

4. Chickering, D.M.: Optimal structure identification with greedy search. J. Mach. Learn. Res. 3, 507–554 (2003)

5. Spirtes, P., Glymour, C.N., Scheines, R.: Causation, Prediction, and Search, 2nd edn. MIT Press, Cambridge (2000)

Cited by 167 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A socio-technical perspective on software vulnerabilities: A causal analysis;Information and Software Technology;2024-12

2. Federated Learning of Generalized Linear Causal Networks;IEEE Transactions on Pattern Analysis and Machine Intelligence;2024-10

3. Bayesian network structure learning using scatter search;Knowledge-Based Systems;2024-09

4. Causal Inference with Latent Variables: Recent Advances and Future Prospectives;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

5. Learning Causal Networks from Episodic Data;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24