Сравнение методов

	Байесовские непараметрические методы	Гауссовский процесс
Область	Байесовские методы	Машинное обучение
Семейство	Bayesian methods	Machine learning
Год появления	1973 (DP); 2006 (GP canonical text)	2006 (book); roots in Kriging, 1951)
Автор метода	Ferguson (Dirichlet Process, 1973); Rasmussen & Williams (GP, 2006)	Rasmussen, C. E. & Williams, C. K. I.
Тип	Bayesian nonparametric model	Probabilistic non-parametric model
Основополагающий источник	Rasmussen, C.E. & Williams, C.K.I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0262182539	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
Другие названия	BNP, Dirichlet process mixture, DPM, Gaussian process regression	GP, Gaussian Process Regression, GPR, Kriging
Связанные	3	3
Сводка	Bayesian nonparametric methods are a family of flexible Bayesian models in which model complexity is not fixed in advance but grows automatically with the data. The two most widely used members are the Dirichlet Process Mixture (DPM), which clusters observations without pre-specifying the number of clusters, and Gaussian Process (GP) regression, which places a prior directly over functions and performs regression or classification without committing to a parametric form. Both frameworks were formalised in the Bayesian nonparametric literature, with the canonical GP treatment given by Rasmussen and Williams (2006).	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.