방법 비교

	베이즈안 온라인 학습	베이지안 로지스틱 회귀
분야	머신러닝	베이지안
계열	Machine learning	Bayesian methods
기원 연도	1990s–2000s	2008
창시자	Opper, M.; Sato, M. (among key contributors)	Gelman, Jakulin, Pittau & Su (weakly-informative prior framework, 2008)
유형	Probabilistic sequential learning	Bayesian classification model
원전	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.
별칭	online Bayesian inference, sequential Bayesian learning, recursive Bayesian estimation, BOL	bayesian binary logistic regression, bayesian classification model, Bayesian Lojistik Regresyon
관련	6	3
요약	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.