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	Online Gaussisk Process	Bayesiansk linjär regression
Ämnesområde	Maskininlärning	Bayesiansk statistik
Familj	Machine learning	Bayesian methods
Ursprungsår	2002	2013 (modern reference); foundations 18th–19th century
Upphovsperson	Csató, L. & Opper, M.	Thomas Bayes / Pierre-Simon Laplace (foundations); modern workflow codified by Gelman et al.
Typ	Bayesian nonparametric model (sequential/online)	Bayesian linear model
Ursprungskälla	Csató, L. & Opper, M. (2002). Sparse on-line Gaussian processes. Neural Computation, 14(3), 641–668.	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955
Alias	OGP, sparse online GP, sequential Gaussian process, incremental Gaussian process	bayesian linear model, probabilistic linear regression, Bayesçi Doğrusal Regresyon
Närliggande	3	4
Sammanfattning	Online Gaussian Process (OGP) extends the Bayesian nonparametric GP framework to streaming or sequentially arriving data. Instead of recomputing the full GP posterior from scratch as each observation arrives, OGP maintains a compact summary — a sparse set of inducing points — and updates it incrementally, making probabilistic regression and classification feasible in real-time and large-scale settings.	Bayesian linear regression is a probabilistic extension of the ordinary linear model, introduced through Bayes' rule and formalised in its modern computational workflow by Gelman et al. (2013). Rather than returning a single point estimate for each coefficient, it combines a user-specified prior distribution with the likelihood of the observed data to produce a full posterior distribution over all parameters, from which credible intervals and posterior predictive distributions are derived.