Comparer des méthodes

	Régression bayésienne	Estimateur de survie de Kaplan-Meier
Domaine	Bayésien	Analyse de survie
Famille	Bayesian methods	Survival analysis
Année d'origine	—	1958
Auteur d'origine	—	Kaplan, E. L. & Meier, P.
Type	Bayesian linear model	Non-parametric survival estimator
Source fondatrice	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955	Kaplan, E. L. & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481.
Alias	bayesian linear regression, probabilistic regression, bayesian regresyon	product-limit estimator, km curve, kaplan-meier sağkalım analizi
Apparentées	2	2
Résumé	Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.	The Kaplan-Meier estimator, introduced by Kaplan and Meier in 1958, is a non-parametric method that estimates the survival curve — the probability of remaining event-free over time — from right-censored time-to-event data. The log-rank test is the companion procedure used to compare survival curves between groups.