Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions.
Toni KarvonenGeorge WynneFilip TronarpChris J. OatesSimo SärkkäPublished in: CoRR (2020)
Keyphrases
- gaussian process
- maximum likelihood estimation
- mixture of gaussians
- marginal likelihood
- expectation propagation
- hyperparameters
- covariance function
- em algorithm
- gaussian processes
- sparse approximations
- gaussian process regression
- maximum likelihood
- bayesian framework
- model selection
- expectation maximization
- regression model
- parameter estimation
- closed form
- approximate inference
- semi supervised
- probability distribution
- latent variables
- bayesian inference
- mixture model
- density function
- generative model
- density estimation
- maximum a posteriori
- markov random field
- support vector