Comparer des méthodes

	Gaussian Process en ligne	Inférence variationnelle
Domaine	Apprentissage automatique	Bayésien
Famille	Machine learning	Bayesian methods
Année d'origine	2002	1999
Auteur d'origine	Csató, L. & Opper, M.	Jordan, Ghahramani, Jaakkola & Saul
Type	Bayesian nonparametric model (sequential/online)	Approximate Bayesian inference
Source fondatrice	Csató, L. & Opper, M. (2002). Sparse on-line Gaussian processes. Neural Computation, 14(3), 641–668.	Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999). An introduction to variational methods for graphical models. Machine Learning, 37(2), 183–233.
Alias	OGP, sparse online GP, sequential Gaussian process, incremental Gaussian process	VI, variational Bayes, VB, mean-field variational inference
Apparentées	3	4
Résumé	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.	Variational inference (VI) is a family of techniques that turn Bayesian posterior computation into an optimisation problem. Instead of drawing samples from the exact posterior — as Markov chain Monte Carlo does — VI posits a simpler, tractable family of distributions and finds the member of that family closest to the true posterior by maximising the evidence lower bound (ELBO). Introduced in its modern graphical-model form by Jordan, Ghahramani, Jaakkola and Saul (1999) and given a comprehensive statistical treatment by Blei, Kucukelbir and McAuliffe (2017), VI is now the standard scalable inference engine in probabilistic machine learning.