Porównaj metody

	Bayesowska inferencja statystyczna	Regresja logistyczna
Dziedzina	Statystyka w badaniach	Statystyka w badaniach
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	1763	1958
Twórca	Thomas Bayes	David Roxbee Cox
Typ	Method	Method
Źródło pierwotne	Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society, 53, 370–418.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Inne nazwy	Bayes theorem, Bayesian inference, posterior probability	logit model, binomial logistic regression, LR
Pokrewne	3	3
Podsumowanie	Bayesian inference is a statistical framework using Bayes' theorem to update beliefs about parameters or hypotheses as data accumulate. Published posthumously in 1763, Thomas Bayes' work lay dormant until the 20th century, when computational advances (Gibbs sampling, Markov Chain Monte Carlo) made Bayesian methods practical. Unlike frequentist inference (which treats parameters as fixed unknowns), Bayesian analysis treats parameters as random variables with probability distributions, enabling direct probability statements about parameters, incorporation of prior knowledge, and sequential updating. Essential in precision medicine, adaptive trials, complex hierarchical models, and any context where prior information enriches inference.	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.