Compara mètodes

	Aprenentatge actiu bayesià	Regressió Logística Bayesiana
Camp	Aprenentatge automàtic	Bayesià
Família	Machine learning	Bayesian methods
Any d'origen	1992–2011	2008
Autor original	MacKay, D.J.C.; Houlsby, N. et al.	Gelman, Jakulin, Pittau & Su (weakly-informative prior framework, 2008)
Tipus	Active learning with Bayesian uncertainty	Bayesian classification model
Font seminal	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.
Àlies	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
Relacionats	6	3
Resum	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.