Statements (50)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:stochastic_process
|
| gptkbp:application |
Time series analysis
Geostatistics Spatial statistics Active learning Surrogate modeling |
| gptkbp:approximationMethod |
Inducing points
Sparse Gaussian Process |
| gptkbp:component |
Kernel function
Covariance function Mean function |
| gptkbp:defines |
A collection of random variables, any finite number of which have a joint Gaussian distribution
|
| gptkbp:field |
gptkb:Probability_theory
Statistics Machine learning |
| gptkbp:form |
f(x) ~ GP(m(x), k(x, x'))
|
| https://www.w3.org/2000/01/rdf-schema#label |
Gaussian Process
|
| gptkbp:inferenceMethod |
Bayesian inference
|
| gptkbp:introducedIn |
20th century
|
| gptkbp:limitation |
Computationally expensive for large datasets
|
| gptkbp:namedAfter |
gptkb:Carl_Friedrich_Gauss
|
| gptkbp:property |
Defined by mean function and covariance function
Non-parametric |
| gptkbp:relatedTo |
gptkb:Gaussian_distribution
gptkb:Kernel_methods Bayesian statistics Brownian motion Gaussian noise Kriging Uncertainty quantification Covariance matrix Random process Hyperparameters Function space view Marginal likelihood Multivariate normal distribution Posterior distribution Prediction interval Prior distribution Reproducing kernel Hilbert space |
| gptkbp:software |
gptkb:GPflow
gptkb:GPy gptkb:Stan gptkb:scikit-learn |
| gptkbp:usedFor |
Bayesian optimization
Regression Classification Function approximation |
| gptkbp:bfsParent |
gptkb:Bayesian_Nonparametrics
|
| gptkbp:bfsLayer |
7
|