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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
]