IndependentNormalWishartPrior.coefficient_mean

IndependentNormalWishartPrior.coefficient_mean(context)

Each variable’s own first lag at persistence, everything else zero.

Delegates to MinnesotaPrior.coefficient_mean() through _minnesota().

Parameters:

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

Return type:

NDArray[double]

Returns:

A (width, k) array in design-column order.

Raises:

• SpecificationError – If a hyperparameter is out of range.

• DimensionError – If persistence has the wrong length.

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)
... )
>>> IndependentNormalWishartPrior().coefficient_mean(ctx)
array([[0., 0.],
       [1., 0.],
       [0., 1.]])