probnum.randprocs.kernels

Kernels or covariance functions.

Kernels describe the spatial or temporal variation of a random process. If evaluated at two sets of points a kernel is defined as the covariance of the values of the random process at these locations.

Kernels support basic algebraic operations, including scaling, addition and multiplication.

Classes

Kernel(input_shape[, output_shape])	(Cross-)covariance function(s)
IsotropicMixin()	Mixin for isotropic kernels.
WhiteNoise(input_shape[, sigma_sq])	White noise kernel.
Linear(input_shape[, constant])	Linear kernel.
Polynomial(input_shape[, constant, exponent])	Polynomial kernel.
ExpQuad(input_shape[, lengthscale])	Exponentiated quadratic / RBF kernel.
RatQuad(input_shape[, lengthscale, alpha])	Rational quadratic kernel.
Matern(input_shape[, lengthscale, nu])	Matern kernel.
ProductMatern(input_shape, lengthscales, nus)	Product Matern kernel.

Class Inheritance Diagram