Sammenlign metoder

	Bayesiansk Support Vector Machine	Bayesiansk Naive Bayes
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	2001–2011	1960s (base); Bayesian parameter treatment formalized 2000s
Ophavsperson	Polson, N. G. & Scott, S. L.; Tipping, M. E.	Naive Bayes: Maron & Kuhns (1960); full Bayesian treatment formalized by Murphy (2012) and Bishop (2006)
Type	Bayesian probabilistic classifier / regressor	Probabilistic generative classifier
Oprindelig kilde	Polson, N. G., & Scott, S. L. (2011). Data augmentation for support vector machines. Bayesian Analysis, 6(1), 1–23.	Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective (Ch. 3, 4). MIT Press. ISBN: 978-0-262-01802-9
Aliasser	Bayesian SVM, probabilistic SVM, Bayesian kernel machine, BSVM	Bayesian NB, Naive Bayes with Bayesian parameter estimation, Dirichlet-Multinomial Naive Bayes, BNB
Relaterede	3	4
Resumé	Bayesian SVM places a prior distribution over the weight vector of a standard SVM and derives a full posterior, enabling calibrated uncertainty estimates, automatic hyperparameter selection, and probabilistic predictions. It combines the strong margin-based geometric intuition of SVMs with the principled uncertainty quantification of Bayesian inference.	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.