Index of modules


B
Block_diag [Gpr]

C
Co_variance_predictor [Gpr_interfaces.Sigs.Eval]
Module for making (co-)variance predictions
Cov_const [Gpr]
Cov_lin_ard [Gpr]
Cov_lin_one [Gpr]
Cov_sampler [Gpr_interfaces.Sigs.Eval]
Module for sampling (multiple) points from the posterior distribution accounting for their covariance
Cov_se_fat [Gpr]
Cov_se_iso [Gpr]
Covariances [Gpr_interfaces.Sigs.Eval]
Posterior covariances

D
Deriv [Gpr_interfaces.Sigs.Deriv]
Sub-modules for learning with derivatives.
Deriv [Gpr_cov_se_iso]
Deriv [Gpr_cov_se_fat]
Deriv [Gpr_cov_lin_one]
Deriv [Gpr_cov_lin_ard]
Deriv [Gpr_cov_const]
Dim_hyper [Gpr_cov_se_fat]

E
Eval [Gpr_interfaces.Sigs.Optimizer]
Sub-modules for learning without derivatives.
Eval [Gpr_interfaces.Sigs.Deriv]
Sub-modules for learning without derivatives.
Eval [Gpr_interfaces.Specs.Optimizer]
Derivatives always require evaluation functions
Eval [Gpr_interfaces.Specs.Deriv]
Derivatives always require evaluation functions
Eval [Gpr_cov_se_iso]
Eval [Gpr_cov_se_fat]
Eval [Gpr_cov_lin_one]
Eval [Gpr_cov_lin_ard]
Eval [Gpr_cov_const]

F
FIC [Gpr_fitc_gp.Make_deriv]
FIC [Gpr_fitc_gp.Make]
FITC [Gpr_fitc_gp.Make_deriv]
FITC [Gpr_fitc_gp.Make]
Fitc_gp [Gpr]

G
Gpr
Gpr_block_diag
Type of block diagonal matrices
Gpr_cov_const
Covariance of a constant function
Gpr_cov_lin_ard
Covariance of linear functions with Automatic Relevance Determination
Gpr_cov_lin_one
Covariance of linear functions with one hyperparameter
Gpr_cov_se_fat
Feature-rich ("fat") squared exponential covariance
Gpr_cov_se_iso
Isotropic squared exponential covariance
Gpr_fitc_gp
Evaluation
Gpr_interfaces
Representations of (sparse) derivative matrices
Gpr_utils
Gpr_version
Gsl [Gpr_interfaces.Sigs.Deriv.Deriv.Optim]
Optimization with the GNU Scientific library (GSL)

H
Hyper [Gpr_interfaces.Specs.Deriv]
Hyper parameters that have derivatives
Hyper_repr [Gpr_cov_se_fat]

I
Inducing [Gpr_interfaces.Sigs.Deriv.Deriv]
Module for inducing inputs with derivatives
Inducing [Gpr_interfaces.Sigs.Eval]
Evaluating inducing inputs
Inducing [Gpr_interfaces.Specs.Deriv]
Derivatives of the covariance matrix of inducing inputs
Inducing [Gpr_interfaces.Specs.Eval]
Signature for evaluating inducing inputs
Inducing_hyper [Gpr_cov_se_fat]
Input [Gpr_interfaces.Sigs.Eval]
Evaluating single inputs
Input [Gpr_interfaces.Specs.Optimizer]
Input [Gpr_interfaces.Specs.Eval]
Signature for evaluating single inputs
Inputs [Gpr_interfaces.Sigs.Deriv.Deriv]
Module for inputs with derivatives
Inputs [Gpr_interfaces.Sigs.Eval]
Evaluating (multiple) inputs
Inputs [Gpr_interfaces.Specs.Optimizer]
Inputs [Gpr_interfaces.Specs.Deriv]
Derivatives of the (cross-) covariance matrix of inputs.
Inputs [Gpr_interfaces.Specs.Eval]
Signature for evaluating multiple inputs
Int_vec [Gpr_utils]
Interfaces [Gpr]

K
Kernel [Gpr_interfaces.Specs.Eval]
Kernel used for evaluation

M
Make [Gpr_fitc_gp]
Make_FIC [Gpr_fitc_gp]
Make_FIC_deriv [Gpr_fitc_gp]
Make_FITC [Gpr_fitc_gp]
Make_FITC_deriv [Gpr_fitc_gp]
Make_deriv [Gpr_fitc_gp]
Make_variational_FIC [Gpr_fitc_gp]
Make_variational_FIC_deriv [Gpr_fitc_gp]
Make_variational_FITC [Gpr_fitc_gp]
Make_variational_FITC_deriv [Gpr_fitc_gp]
Mean [Gpr_interfaces.Sigs.Eval]
Posterior mean for a single input
Mean_predictor [Gpr_interfaces.Sigs.Eval]
Module for making mean predictions
Means [Gpr_interfaces.Sigs.Eval]
Posterior means for (multiple) inputs
Model [Gpr_interfaces.Sigs.Deriv.Deriv]
(Untrained) model with derivative information
Model [Gpr_interfaces.Sigs.Eval]
(Untrained) model - does not require targets

O
Optim [Gpr_interfaces.Sigs.Deriv.Deriv]
Optimization module for evidence maximization
Optimizer [Gpr_interfaces.Sigs.Optimizer]
Sub-modules for global optimization.

P
Params [Gpr_cov_se_iso]
Params [Gpr_cov_se_fat]
Params [Gpr_cov_lin_one]
Params [Gpr_cov_lin_ard]
Params [Gpr_cov_const]
Proj_hyper [Gpr_cov_se_fat]

S
SGD [Gpr_interfaces.Sigs.Deriv.Deriv.Optim]
SMD [Gpr_interfaces.Sigs.Deriv.Deriv.Optim]
Sampler [Gpr_interfaces.Sigs.Eval]
Module for sampling single points from the posterior distribution
Sigs [Gpr_interfaces]
Signatures for learning sparse Gaussian processes with inducing inputs
Sparse_indices [Gpr_interfaces]
Representation of indices into sparse matrices
Spec [Gpr_interfaces.Sigs.Optimizer.Optimizer]
Spec [Gpr_interfaces.Sigs.Deriv.Deriv]
Specification of covariance function derivatives
Spec [Gpr_interfaces.Sigs.Eval]
Specification of covariance function
Specs [Gpr_interfaces]
Specifications of covariance functions (= kernels) and their derivatives
Stats [Gpr_interfaces.Sigs.Eval]
Statistics derived from trained models

T
Test [Gpr_interfaces.Sigs.Deriv.Deriv]
Module for testing derivative code
Trained [Gpr_interfaces.Sigs.Deriv.Deriv]
Trained model with derivative information
Trained [Gpr_interfaces.Sigs.Eval]
Trained model - requires targets

U
Utils [Gpr]

V
Var [Gpr_interfaces.Specs.Optimizer]
Input parameters that have derivatives
Variance [Gpr_interfaces.Sigs.Eval]
Posterior variance for a single input
Variances [Gpr_interfaces.Sigs.Eval]
Posterior variances for (multiple) inputs
Variational_FIC [Gpr_fitc_gp.Make_deriv]
Variational_FIC [Gpr_fitc_gp.Make]
Variational_FITC [Gpr_fitc_gp.Make_deriv]
Variational_FITC [Gpr_fitc_gp.Make]
Version [Gpr]