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.


Kernel(input_dim[, output_dim])

Kernel / covariance function.

WhiteNoise(input_dim[, sigma])

White noise kernel.

Linear(input_dim[, constant])

Linear kernel.

Polynomial(input_dim[, constant, exponent])

Polynomial kernel.

ExpQuad(input_dim[, lengthscale])

Exponentiated quadratic / RBF kernel.

RatQuad(input_dim[, lengthscale, alpha])

Rational quadratic kernel.

Matern(input_dim[, lengthscale, nu])

Matern kernel.

Class Inheritance Diagram

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