Product

class tinygp.kernels.quasisep.Product(kernel1: Quasisep, kernel2: Quasisep)

Bases: Quasisep

A helper to represent the product of two quasiseparable kernels

coord_to_sortable(X: tinygp.helpers.JAXArray) → tinygp.helpers.JAXArray

We assume that both kernels use the same coordinates

design_matrix() → tinygp.helpers.JAXArray

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

The observation model for the process

stationary_covariance() → tinygp.helpers.JAXArray

The stationary covariance of the process

to_general_qsm(X1: tinygp.helpers.JAXArray, X2: tinygp.helpers.JAXArray) → GeneralQSM

The generalized quasiseparable representation of this kernel

to_symm_qsm(X: tinygp.helpers.JAXArray) → SymmQSM

The symmetric quasiseparable representation of this kernel

transition_matrix(X1: tinygp.helpers.JAXArray, X2: tinygp.helpers.JAXArray) → tinygp.helpers.JAXArray

The adjoint transition matrix between two coordinates.

If a column-state mean propagates from X1 to X2 as m2 = F @ m1, this method must return F.T. Equivalently, tinygp’s Kalman implementation propagates means using transition_matrix(X1, X2).T @ m1.