Salīdzināt metodes

	Algoritms GES	Bayes' tīkls
Nozare	Cēloņsakarību secināšana	Bajesa metodes
Saime	Machine learning	Bayesian methods
Izcelsmes gads	2002	1988
Autors	David Maxwell Chickering	Judea Pearl
Tips	Score-based causal structure learning algorithm	Probabilistic graphical model
Pirmavots	Chickering, D. M. (2002). Optimal structure identification with greedy search. Journal of Machine Learning Research, 3, 507–554.	Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797
Citi nosaukumi	Greedy Equivalence Search, GES Causal Discovery, Score-Based Greedy Search, Açgözlü Eşdeğerlik Araması	Bayes network, belief network, probabilistic graphical model, directed graphical model
Saistītās	2	4
Kopsavilkums	Greedy Equivalence Search (GES) is a score-based algorithm for learning the causal structure of a set of variables from observational data. Introduced by David Maxwell Chickering in 2002, GES operates directly on Markov equivalence classes of directed acyclic graphs (DAGs), represented as completed partially directed acyclic graphs (CPDAGs). Under the assumptions of causal sufficiency and a faithful data-generating process, GES is proven to recover the true equivalence class in the large-sample limit.	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.