Salīdzināt metodes

	FCI algoritms	Bayes' tīkls
Nozare	Cēloņsakarību secināšana	Bajesa metodes
Saime	Machine learning	Bayesian methods
Izcelsmes gads	2000	1988
Autors	Spirtes, Glymour & Scheines	Judea Pearl
Tips	Constraint-based causal discovery algorithm	Probabilistic graphical model
Pirmavots	Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0-262-19440-2	Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797
Citi nosaukumi	FCI, Fast Causal Inference, FCI Causal Discovery, FCI Algoritması	Bayes network, belief network, probabilistic graphical model, directed graphical model
Saistītās	2	4
Kopsavilkums	The Fast Causal Inference (FCI) algorithm is a constraint-based causal discovery method introduced by Spirtes, Glymour, and Scheines in their landmark 2000 book Causation, Prediction, and Search. Unlike its predecessor the PC algorithm, FCI is specifically designed to handle the presence of latent (unmeasured) common causes and sample selection bias. It outputs a Partial Ancestral Graph (PAG), which faithfully represents the set of all causal structures consistent with the observed conditional independencies.	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.