Comparer des méthodes

	Réseau bayésien	Analyse de survie
Domaine	Bayésien	Statistiques de recherche
Famille	Bayesian methods	Process / pipeline
Année d'origine	1988	1958
Auteur d'origine	Judea Pearl	Edward L. Kaplan and Paul Meier
Type	Probabilistic graphical model	Method
Source fondatrice	Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
Alias	Bayes network, belief network, probabilistic graphical model, directed graphical model	Kaplan-Meier analysis, Cox regression, TTE analysis
Apparentées	4	3
Résumé	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.	Survival analysis is a collection of statistical methods for modeling time from a defined starting point until an event of interest occurs (disease, recovery, death, equipment failure). Kaplan and Meier's nonparametric estimator (1958) and David Cox's proportional hazards model (1972) jointly enabled analysis of censored data—individuals whose event times are unknown because they left the study or were still event-free at follow-up. Indispensable in oncology, cardiology, infectious disease research, engineering reliability, and any field where time-to-event matters.