Exp#

class tinygp.kernels.quasisep.Exp(scale: tinygp.helpers.JAXArray, sigma: tinygp.helpers.JAXArray = <factory>)[source]#

Bases: Quasisep

A scalable implementation of tinygp.kernels.stationary.Exp

This kernel takes the form:

\[k(\tau)=\sigma^2\,\exp\left(-\frac{\tau}{\ell}\right)\]

for \(\tau = |x_i - x_j|\).

Parameters:

  • scale – The parameter \(\ell\).

  • sigma – The parameter \(\sigma\).

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

A helper function used to convert coordinates to sortable 1-D values

By default, this is the identity, but in cases where X is structured (e.g. multivariate inputs), this can be used to appropriately unwrap that structure.

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) → 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[source]#

The transition matrix between two coordinates