Võrdle meetodeid

	Bayesi võrk	NOTEARS: pidev optimeerimine põhjusliku struktuuri õppimiseks
Valdkond	Bayesi meetodid	Põhjuslik järeldamine
Perekond	Bayesian methods	Machine learning
Tekkeaasta	1988	2018
Looja	Judea Pearl	Zheng, Aragam, Ravikumar & Xing
Tüüp	Probabilistic graphical model	Continuous optimization algorithm for causal DAG discovery
Algallikas	Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797	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.
Rööpnimetused	Bayes network, belief network, probabilistic graphical model, directed graphical model	DAGs with NO TEARS, Continuous Structure Learning, Continuous DAG Optimization, Sürekli DAG Yapı Öğrenimi
Seotud	4	1
Kokkuvõte	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.	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.