So sánh phương pháp

	NOTEARS: Tối ưu hóa liên tục để học cấu trúc nhân quả	Mạng Bayes
Lĩnh vực	Suy luận nhân quả	Bayes
Họ	Machine learning	Bayesian methods
Năm ra đời	2018	1988
Người khởi xướng	Zheng, Aragam, Ravikumar & Xing	Judea Pearl
Loại	Continuous optimization algorithm for causal DAG discovery	Probabilistic graphical model
Công trình gốc	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
Tên gọi khác	DAGs with NO TEARS, Continuous Structure Learning, Continuous DAG Optimization, Sürekli DAG Yapı Öğrenimi	Bayes network, belief network, probabilistic graphical model, directed graphical model
Liên quan	1	4
Tóm tắt	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.