Porovnat metody

	Bayesovská logistická regrese	Logistická regrese	Markov Chain Monte Carlo (MCMC)
Obor	Bayesovská statistika	Statistika ve výzkumu	Bayesovská statistika
Rodina	Bayesian methods	Process / pipeline	Bayesian methods
Rok vzniku	2008	1958	—
Tvůrce	Gelman, Jakulin, Pittau & Su (weakly-informative prior framework, 2008)	David Roxbee Cox	—
Typ	Bayesian classification model	Method	Posterior sampling algorithm
Původní zdroj	Gelman, A., Jakulin, A., Pittau, M. G. & Su, Y.-S. (2008). A Weakly Informative Default Prior Distribution for Logistic and Other Regression Models. Annals of Applied Statistics, 2(4), 1360–1383.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	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
Další názvy	bayesian binary logistic regression, bayesian classification model, Bayesian Lojistik Regresyon	logit model, binomial logistic regression, LR	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)
Příbuzné	3	3	3
Shrnutí	Bayesian logistic regression is a classification model that applies Bayesian inference to a logistic (sigmoid) likelihood for binary or multinomial outcomes. Developed within the weakly-informative prior framework formalised by Gelman, Jakulin, Pittau and Su (2008), it places a prior distribution over the coefficients and combines that prior with the data likelihood to yield a full posterior distribution for each parameter — delivering calibrated class probabilities and honest uncertainty even in small samples, rare-event settings, or cases of complete separation where frequentist maximum likelihood estimation collapses.	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.	Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model.