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

Inheritance diagram of probnum.randprocs.kernels.Kernel, probnum.randprocs.kernels.IsotropicMixin, probnum.randprocs.kernels.WhiteNoise, probnum.randprocs.kernels.Linear, probnum.randprocs.kernels.Polynomial, probnum.randprocs.kernels.ExpQuad, probnum.randprocs.kernels.RatQuad, probnum.randprocs.kernels.Matern, probnum.randprocs.kernels.ProductMatern