Porovnat metody

	Bayesovské online učení	Bayesovská logistická regrese
Obor	Strojové učení	Bayesovská statistika
Rodina	Machine learning	Bayesian methods
Rok vzniku	1990s–2000s	2008
Tvůrce	Opper, M.; Sato, M. (among key contributors)	Gelman, Jakulin, Pittau & Su (weakly-informative prior framework, 2008)
Typ	Probabilistic sequential learning	Bayesian classification model
Původní zdroj	Opper, M. (1998). A Bayesian approach to on-line learning. In D. Saad (Ed.), On-Line Learning in Neural Networks (pp. 363–378). Cambridge University Press.	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.
Další názvy	online Bayesian inference, sequential Bayesian learning, recursive Bayesian estimation, BOL	bayesian binary logistic regression, bayesian classification model, Bayesian Lojistik Regresyon
Příbuzné	6	3
Shrnutí	Bayesian online learning applies Bayesian inference sequentially: each time a new observation arrives, the current posterior over model parameters becomes the prior for the next update. The result is a principled probabilistic framework that maintains calibrated uncertainty estimates throughout, making it well-suited for streaming and non-stationary data settings.	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.