# Conditioned#

class tinygp.means.Conditioned(X: tinygp.helpers.JAXArray, alpha: tinygp.helpers.JAXArray, kernel: tinygp.kernels.base.Kernel, include_mean: bool, mean_function: Optional[tinygp.means.Mean] = None)[source]#

Bases: object

The mean of a process conditioned on observed data

Parameters
• X – The coordinates of the data. alpha: The value $$L^-1\,y$$ where L

• the (is scale_tril and y is) – observed data.

• scale_tril – The lower Cholesky factor of the base process’ kernel matrix.

• kernel – The predictive kerenl; this will generally be the kernel from the kernel used by the original process.

• include_mean – If True, the predicted values will include the mean function evaluated at X_test.

• mean_function – The mean function of the base process. Used only if include_mean is True.