SumOfCoefficientsPrior.coefficient_mean#

SumOfCoefficientsPrior.coefficient_mean(context)[source]#

Zero: this prior’s content is entirely in its rows.

A component with no moment content reports a zero mean so that composition, which averages means by precision, is unaffected by it; with infinite variance the precision is zero and the mean is never weighted.

Parameters:

context (_PriorContext) – The sample description; width and k_endog are read.

Return type:

NDArray[double]

Returns:

A (width, k) array of zeros.

Example

>>> import numpy as np
>>> from cultivars.bayes.priors import PriorContext
>>> ctx = PriorContext(
...     k_endog=2, order=1, scales=np.ones(2), presample_mean=np.zeros(2)
... )
>>> SumOfCoefficientsPrior().coefficient_mean(ctx)
array([[0., 0.],
       [0., 0.],
       [0., 0.]])