Celerite#
- class tinygp.kernels.quasisep.Celerite(a: tinygp.helpers.JAXArray, b: tinygp.helpers.JAXArray, c: tinygp.helpers.JAXArray, d: tinygp.helpers.JAXArray)[source]#
Bases:
Quasisep
The baseline kernel from the
celerite
packageThis form of the kernel was introduced by Foreman-Mackey et al. (2017), and implemented in the celerite package. It shouldn’t generally be used on its own, and other kernels described in this subpackage should generally be preferred.
This kernel takes the form:
\[k(\tau)=\exp(-c\,\tau)\,\left[a\,\cos(d\,\tau)+b\,\sin(d\,\tau)\right]\]for \(\tau = |x_i - x_j|\).
In order to be positive definite, the parameters of this kernel must satisfy \(a\,c - b\,d > 0\), and you will see NaNs if you use parameters that don’t satisfy this relationship.
- 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.
- 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
- to_general_qsm(X1: tinygp.helpers.JAXArray, X2: tinygp.helpers.JAXArray) GeneralQSM #
The generalized quasiseparable representation of this kernel