# statsmodels.regression.process_regression.ProcessMLE.covariance¶

method

ProcessMLE.covariance(time, scale_params, smooth_params, scale_data, smooth_data)[source]

Returns a Gaussian process covariance matrix.

Parameters
timearray-like

The time points at which the fitted covariance matrix is calculated.

scale_paramsarray-like

The regression parameters for the scaling part of the covariance structure.

smooth_paramsarray-like

The regression parameters for the smoothing part of the covariance structure.

The data used to determine the scale parameter, must have len(time) rows.

smooth_data: Dataframe

The data used to determine the smoothness parameter, must have len(time) rows.

Returns
A covariance matrix.

Notes

If the model was fit using formulas, scale and smooth should be Dataframes, containing all variables that were present in the respective scaling and smoothing formulas used to fit the model. Otherwise, scale and smooth should contain data arrays whose columns align with the fitted scaling and smoothing parameters.

The covariance is only for the Gaussian process and does not include the white noise variance.