Сравнение методов

	Байесовский наивный классификатор	Гауссовский процесс
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	1960s (base); Bayesian parameter treatment formalized 2000s	2006 (book); roots in Kriging, 1951)
Автор метода	Naive Bayes: Maron & Kuhns (1960); full Bayesian treatment formalized by Murphy (2012) and Bishop (2006)	Rasmussen, C. E. & Williams, C. K. I.
Тип	Probabilistic generative classifier	Probabilistic non-parametric model
Основополагающий источник	Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective (Ch. 3, 4). MIT Press. ISBN: 978-0-262-01802-9	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
Другие названия	Bayesian NB, Naive Bayes with Bayesian parameter estimation, Dirichlet-Multinomial Naive Bayes, BNB	GP, Gaussian Process Regression, GPR, Kriging
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.	A Gaussian Process (GP) is a non-parametric, fully probabilistic machine learning model that places a prior distribution directly over functions. Rather than predicting a single value, it returns a predictive mean and a calibrated uncertainty estimate at every test point, making it especially valuable for regression on small to medium datasets and for Bayesian optimization tasks.