Can We Trust Bayesian Uncertainty Quantification from Gaussian Process Priors with Squared Exponential Covariance Kernel?
Amine HadjiBotond SzabóPublished in: SIAM/ASA J. Uncertain. Quantification (2021)
Keyphrases
- gaussian process
- gaussian processes
- bayesian framework
- gaussian process regression
- hyperparameters
- covariance function
- reproducing kernel hilbert space
- dirichlet process
- prior distribution
- posterior distribution
- dynamical models
- posterior probability
- bayesian methods
- regression model
- variational inference
- semi supervised
- marginal likelihood
- model selection
- approximate inference
- expectation propagation
- dynamical model
- prior information
- latent variables
- fully bayesian
- kernel logistic regression
- gaussian process models
- sparse approximations
- bayesian inference
- maximum a posteriori
- generative model
- multi task learning
- support vector
- conditional probabilities
- covariance matrix
- least squares
- pairwise
- bayesian networks
- machine learning