covariance_kernels (pmrf.covariance_kernels)

Covariance kernels for Gaussian processes.

Useful for discrepancy modeling. See pmrf.discrepancy_models for more details.

Classes

AbstractCovarianceKernel(*[, name, metadata])	Abstract base class for covariance kernel functions.
AutoCrossKernel(auto, cross, num_outputs, *)	Kernel that routes between a auto-correlation and cross-correlation kernels.
ConstantKernel(variance, *[, name, metadata])	Kernel that returns a constant variance.
Matern32Kernel(lengthscale, *[, name, metadata])	Matérn kernel with nu=3/2.
Matern52Kernel(lengthscale, *[, name, metadata])	Matérn kernel with nu=5/2.
PeriodicKernel(period, lengthscale, *[, ...])	Periodic (Exp-Sine-Squared) kernel.
ProductKernel(k1, k2, *[, name, metadata])	Kernel representing the product of two kernels.
RBFKernel(lengthscale, *[, name, metadata])	Radial Basis Function (Squared Exponential) kernel.
SharedIndependentKernel(base_kernel, ...[, ...])	Evaluates a base kernel and broadcasts its output to represent multiple independent dimensions (e.g., real and imaginary parts) withed share hyperparameters.
SumKernel(k1, k2, *[, name, metadata])	Kernel representing the sum of two kernels.
WhiteNoiseKernel(variance, *[, name, metadata])	Kernel representing independent Gaussian noise.
ZeroKernel(*[, name, metadata])	Kernel that always evaluates to zero.