SpikeAndSlabPrior._scale_variance#

SpikeAndSlabPrior._scale_variance(scales, context)[source]#

Spike or slab variance per the current indicators, loose elsewhere.

The raw-coefficient variance the current state implies: each lag coefficient carries \((s_i / s_j)^2 \tau_1^2\) when its indicator is one and \((s_i / s_j)^2 \tau_0^2\) otherwise, and every deterministic and exogenous row carries \((\text{deterministic\_scale} \cdot s_i)^2\). This is the variance the coefficient block is drawn under on the next sweep.

Parameters:

  • scales (dict[str, NDArray[double]]) – The current latent state.

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

Return type:

NDArray[double]

Returns:

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

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)
... )
>>> prior = SpikeAndSlabPrior()
>>> state = prior._initial_scales(ctx, np.full((3, 2), 0.05))
>>> state["gamma"][0, 1] = 0.0
>>> prior._scale_variance(state, ctx)
array([[1.0e+02, 1.0e+02],
       [2.5e-01, 2.5e-05],
       [2.5e-01, 2.5e-01]])