Comparer des méthodes

	NOTEARS : Optimisation Continue pour l'Apprentissage de Structures Causales	Réseau bayésien
Domaine	Inférence causale	Bayésien
Famille	Machine learning	Bayesian methods
Année d'origine	2018	1988
Auteur d'origine	Zheng, Aragam, Ravikumar & Xing	Judea Pearl
Type	Continuous optimization algorithm for causal DAG discovery	Probabilistic graphical model
Source fondatrice	Zheng, X., Aragam, B., Ravikumar, P., & Xing, E. P. (2018). DAGs with NO TEARS: Continuous optimization for structure learning. Advances in Neural Information Processing Systems, 31.	Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797
Alias	DAGs with NO TEARS, Continuous Structure Learning, Continuous DAG Optimization, Sürekli DAG Yapı Öğrenimi	Bayes network, belief network, probabilistic graphical model, directed graphical model
Apparentées	1	4
Résumé	NOTEARS (No Tears: Acyclicity Regression Structure) is a causal structure learning algorithm introduced by Zheng, Aragam, Ravikumar, and Xing in 2018 at NeurIPS. It reformulates the combinatorially hard problem of learning a directed acyclic graph (DAG) from observational data as a continuous, smooth optimization problem, enabling the use of standard gradient-based solvers and removing the need for exhaustive combinatorial search over graph space.	A Bayesian network is a probabilistic graphical model, introduced by Judea Pearl in 1988, that encodes a set of variables and their conditional dependencies as a directed acyclic graph (DAG). Each node represents a variable; each directed edge encodes a direct probabilistic influence. By combining Bayes' rule with the graph's conditional independence structure, the model supports reasoning under uncertainty — computing the probability of any variable given observed evidence about others.