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	बेयसियन ऑनलाइन लर्निंग	बायेसियन गॉसियन प्रक्रिया
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	1990s–2000s	1978–2006
प्रवर्तक	Opper, M.; Sato, M. (among key contributors)	O'Hagan, A.; Neal, R. M.; Rasmussen, C. E. & Williams, C. K. I.
प्रकार	Probabilistic sequential learning	Probabilistic kernel model
मौलिक स्रोत	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
उपनाम	online Bayesian inference, sequential Bayesian learning, recursive Bayesian estimation, BOL	GP regression, GPR, Gaussian process model, GP classifier
संबंधित	6	3
सारांश	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 Bayesian Gaussian Process (GP) places a probability distribution directly over functions, using a kernel to encode similarity between inputs. After observing data, Bayes' rule converts this prior into a posterior that yields not just point predictions but calibrated uncertainty estimates at every new input — making it one of the most principled probabilistic models in machine learning.