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	Metody bayesowskie nieparametryczne	Proces Gaussa
Dziedzina	Statystyka bayesowska	Uczenie maszynowe
Rodzina	Bayesian methods	Machine learning
Rok powstania	1973 (DP); 2006 (GP canonical text)	2006 (book); roots in Kriging, 1951)
Twórca	Ferguson (Dirichlet Process, 1973); Rasmussen & Williams (GP, 2006)	Rasmussen, C. E. & Williams, C. K. I.
Typ	Bayesian nonparametric model	Probabilistic non-parametric model
Źródło pierwotne	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
Inne nazwy	BNP, Dirichlet process mixture, DPM, Gaussian process regression	GP, Gaussian Process Regression, GPR, Kriging
Pokrewne	3	3
Podsumowanie	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.