Salīdzināt metodes

	Tiešsaistes Gausa process	Variacionālā secinājumi
Nozare	Mašīnmācīšanās	Bajesa metodes
Saime	Machine learning	Bayesian methods
Izcelsmes gads	2002	1999
Autors	Csató, L. & Opper, M.	Jordan, Ghahramani, Jaakkola & Saul
Tips	Bayesian nonparametric model (sequential/online)	Approximate Bayesian inference
Pirmavots	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.
Citi nosaukumi	OGP, sparse online GP, sequential Gaussian process, incremental Gaussian process	VI, variational Bayes, VB, mean-field variational inference
Saistītās	3	4
Kopsavilkums	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.