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Bayesovská sieť×Logistická regresia×
OdborBayesovské metódyŠtatistika vo výskume
RodinaBayesian methodsProcess / pipeline
Rok vzniku19881958
TvorcaJudea PearlDavid Roxbee Cox
TypProbabilistic graphical modelMethod
Pôvodný zdrojPearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗
Ďalšie názvyBayes network, belief network, probabilistic graphical model, directed graphical modellogit model, binomial logistic regression, LR
Príbuzné43
ZhrnutieA 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.
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ScholarGatePorovnať metódy: Bayesian Network · Logistic Regression. Získané 2026-06-18 z https://scholargate.app/sk/compare