Product#

class tinygp.kernels.quasisep.Product(kernel1: tinygp.kernels.quasisep.Quasisep, kernel2: tinygp.kernels.quasisep.Quasisep)[source]#

Bases: tinygp.kernels.quasisep.Quasisep

A helper to represent the product of two quasiseparable kernels

coord_to_sortable(X: tinygp.helpers.JAXArray) → tinygp.helpers.JAXArray[source]#

We assume that both kernels use the same coordinates

design_matrix() → tinygp.helpers.JAXArray[source]#

The design matrix for the process

evaluate(X1: tinygp.helpers.JAXArray, X2: tinygp.helpers.JAXArray) → tinygp.helpers.JAXArray#

The kernel evaluated via the quasiseparable representation

evaluate_diag(X: tinygp.helpers.JAXArray) → tinygp.helpers.JAXArray#

For quasiseparable kernels, the variance is simple to compute

observation_model(X: tinygp.helpers.JAXArray) → tinygp.helpers.JAXArray[source]#

The observation model for the process

stationary_covariance() → tinygp.helpers.JAXArray[source]#

The stationary covariance of the process

to_general_qsm(X1: tinygp.helpers.JAXArray, X2: tinygp.helpers.JAXArray) → tinygp.solvers.quasisep.general.GeneralQSM#

The generalized quasiseparable representation of this kernel

to_symm_qsm(X: tinygp.helpers.JAXArray) → tinygp.solvers.quasisep.core.SymmQSM#

The symmetric quasiseparable representation of this kernel

transition_matrix(X1: tinygp.helpers.JAXArray, X2: tinygp.helpers.JAXArray) → tinygp.helpers.JAXArray[source]#

The transition matrix between two coordinates