Statements (52)
| 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 |
gptkb: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'))
|
| 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:Random_process gptkb:Kernel_methods Bayesian statistics Brownian motion Gaussian noise Kriging Uncertainty quantification Covariance matrix 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_Optimization_Algorithm
gptkb:Bayesian_Nonparametrics gptkb:Gaussian_Function |
| gptkbp:bfsLayer |
8
|
| https://www.w3.org/2000/01/rdf-schema#label |
Gaussian Process
|