Comparer des méthodes

	Apprentissage Actif Bayésien	Régression logistique bayésienne
Domaine	Apprentissage automatique	Bayésien
Famille	Machine learning	Bayesian methods
Année d'origine	1992–2011	2008
Auteur d'origine	MacKay, D.J.C.; Houlsby, N. et al.	Gelman, Jakulin, Pittau & Su (weakly-informative prior framework, 2008)
Type	Active learning with Bayesian uncertainty	Bayesian classification model
Source fondatrice	Houlsby, N., Huszár, F., Ghahramani, Z., & Lengyel, M. (2011). Bayesian Active Learning for Classification and Preference Learning. arXiv preprint arXiv:1112.5745.	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.
Alias	BAL, Bayesian optimal experimental design for ML, BALD (Bayesian Active Learning by Disagreement), probabilistic active learning	bayesian binary logistic regression, bayesian classification model, Bayesian Lojistik Regresyon
Apparentées	6	3
Résumé	Bayesian Active Learning (BAL) combines a probabilistic model with an active query strategy to identify the unlabeled examples that, once labeled, would most reduce model uncertainty. Instead of labeling data at random, BAL guides an oracle — typically a human annotator — toward the points where labeling will provide the greatest information gain, making it highly label-efficient.	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.