Statements (52)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:stochastic_process
|
| gptkbp:application |
Bayesian optimization
geostatistics kriging surrogate modeling non-parametric modeling |
| gptkbp:author |
gptkb:Carl_Edward_Rasmussen
gptkb:Christopher_K._I._Williams |
| gptkbp:book |
gptkb:Gaussian_Processes_for_Machine_Learning
|
| gptkbp:characterizedBy |
covariance function
mean function |
| gptkbp:field |
gptkb:machine_learning
gptkb:probability_theory statistics |
| https://www.w3.org/2000/01/rdf-schema#label |
Gaussian processes
|
| gptkbp:inferenceMethod |
Bayesian inference
|
| gptkbp:introducedIn |
20th century
|
| gptkbp:kernelFunction |
Matérn kernel
squared exponential kernel periodic kernel rational quadratic kernel |
| gptkbp:namedAfter |
gptkb:Carl_Friedrich_Gauss
|
| gptkbp:property |
defined by mean and covariance functions
any finite collection of random variables has a joint Gaussian distribution non-parametric closed under linear operations flexible modeling of functions |
| gptkbp:relatedConcept |
Markov chain
isotropy posterior distribution covariance matrix stationarity marginal likelihood hyperparameters predictive distribution random field process prior |
| gptkbp:relatedTo |
gptkb:Gaussian_distribution
gptkb:Wiener_process Brownian motion |
| gptkbp:software |
gptkb:GPflow
gptkb:GPy gptkb:GPyTorch gptkb:scikit-learn |
| gptkbp:usedIn |
gptkb:dictionary
spatial statistics time series analysis regression |
| gptkbp:bfsParent |
gptkb:Kolmogorov's_continuity_theorem
gptkb:Neural_Tangent_Kernel gptkb:Zoubin_Ghahramani |
| gptkbp:bfsLayer |
6
|