Usporedite metode

	Bayesovsko online učenje	Gaussovi procesi
Područje	Strojno učenje	Strojno učenje
Obitelj	Machine learning	Machine learning
Godina nastanka	1990s–2000s	2006 (book); roots in Kriging, 1951)
Tvorac	Opper, M.; Sato, M. (among key contributors)	Rasmussen, C. E. & Williams, C. K. I.
Vrsta	Probabilistic sequential learning	Probabilistic non-parametric model
Temeljni izvor	Opper, M. (1998). A Bayesian approach to on-line learning. In D. Saad (Ed.), On-Line Learning in Neural Networks (pp. 363–378). Cambridge University Press.	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
Drugi nazivi	online Bayesian inference, sequential Bayesian learning, recursive Bayesian estimation, BOL	GP, Gaussian Process Regression, GPR, Kriging
Srodne	6	3
Sažetak	Bayesian online learning applies Bayesian inference sequentially: each time a new observation arrives, the current posterior over model parameters becomes the prior for the next update. The result is a principled probabilistic framework that maintains calibrated uncertainty estimates throughout, making it well-suited for streaming and non-stationary data settings.	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.