Porovnať metódy

	Bayesovská sieť	Logistická regresia	Analýza prežitia
Odbor	Bayesovské metódy	Štatistika vo výskume	Štatistika vo výskume
Rodina	Bayesian methods	Process / pipeline	Process / pipeline
Rok vzniku	1988	1958	1958
Tvorca	Judea Pearl	David Roxbee Cox	Edward L. Kaplan and Paul Meier
Typ	Probabilistic graphical model	Method	Method
Pôvodný zdroj	Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
Ďalšie názvy	Bayes network, belief network, probabilistic graphical model, directed graphical model	logit model, binomial logistic regression, LR	Kaplan-Meier analysis, Cox regression, TTE analysis
Príbuzné	4	3	3
Zhrnutie	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	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.