Statements (50)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:model
statistical analysis |
| gptkbp:advantage |
flexible modeling
provides uncertainty estimates |
| gptkbp:application |
robotics
spatial statistics time series analysis Bayesian optimization active learning environmental modeling geostatistics surrogate modeling |
| gptkbp:basedOn |
gptkb:Gaussian_process
|
| gptkbp:field |
gptkb:machine_learning
statistics |
| https://www.w3.org/2000/01/rdf-schema#label |
Gaussian process regression
|
| gptkbp:implementedIn |
gptkb:GPflow
gptkb:GPy gptkb:Julia gptkb:MATLAB gptkb:TensorFlow_Probability gptkb:scikit-learn R |
| gptkbp:input |
training data
|
| gptkbp:introduced |
gptkb:Carl_Edward_Rasmussen
gptkb:Christopher_K._I._Williams |
| gptkbp:limitation |
computationally expensive for large datasets
scales cubically with number of data points |
| gptkbp:notablePublication |
gptkb:Gaussian_Processes_for_Machine_Learning_(2006)
|
| gptkbp:output |
covariance function
mean function predictive distribution |
| gptkbp:relatedConcept |
gptkb:Gaussian_process_classification
probabilistic modeling hyperparameter optimization kriging covariance function mean function Bayesian linear regression |
| gptkbp:relatedTo |
gptkb:kernel_methods
Bayesian inference support vector machines |
| gptkbp:requires |
hyperparameters
kernel function |
| gptkbp:usedFor |
statistical analysis
function approximation uncertainty quantification non-parametric regression |
| gptkbp:bfsParent |
gptkb:Gaussian_process
|
| gptkbp:bfsLayer |
6
|