A Coreset-based, Tempered Variational Posterior for Accurate and Scalable Stochastic Gaussian Process Inference.
Mert KetenciAdler J. PerotteNoémie ElhadadIñigo UrteagaPublished in: CoRR (2023)
Keyphrases
- gaussian process
- covariance function
- variational inference
- gaussian processes
- approximate inference
- expectation propagation
- bayesian framework
- regression model
- model selection
- hyperparameters
- variational methods
- latent variables
- gaussian process regression
- exact inference
- bayesian inference
- semi supervised
- loopy belief propagation
- gaussian process classification
- variational bayes
- gaussian process models
- fully bayesian
- marginal likelihood
- free energy
- probabilistic inference
- dynamic bayesian networks
- sparse approximations
- pairwise
- bayesian methods
- image segmentation
- posterior distribution
- upper bound
- bayesian networks