Сравнение методов

	Байесовский наивный классификатор	Байесовская логистическая регрессия
Область	Машинное обучение	Байесовские методы
Семейство	Machine learning	Bayesian methods
Год появления	1960s (base); Bayesian parameter treatment formalized 2000s	2008
Автор метода	Naive Bayes: Maron & Kuhns (1960); full Bayesian treatment formalized by Murphy (2012) and Bishop (2006)	Gelman, Jakulin, Pittau & Su (weakly-informative prior framework, 2008)
Тип	Probabilistic generative classifier	Bayesian classification model
Основополагающий источник	Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective (Ch. 3, 4). MIT Press. ISBN: 978-0-262-01802-9	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.
Другие названия	Bayesian NB, Naive Bayes with Bayesian parameter estimation, Dirichlet-Multinomial Naive Bayes, BNB	bayesian binary logistic regression, bayesian classification model, Bayesian Lojistik Regresyon
Связанные	4	3
Сводка	Bayesian Naive Bayes applies a fully Bayesian treatment to the parameters of the classic Naive Bayes classifier: instead of estimating class-conditional distributions by maximum likelihood, it places conjugate priors (typically Dirichlet for categorical data or Gaussian-Gamma for continuous data) over the parameters and integrates them out, producing predictive posterior distributions that naturally quantify uncertainty and avoid overfitting on small datasets.	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.