sampler.GlobalVarianceModel

sampler.GlobalVarianceModel()

Wrapper around methods / functions for sampling a “global” error variance model with inverse gamma prior.

Methods

Name	Description
sample_one_iteration	Sample one iteration of a global error variance parameter

sampler.GlobalVarianceModel.sample_one_iteration(residual, rng, a, b)

Sample one iteration of a global error variance parameter

Name	Type	Description	Default
residual	Residual	`stochtree` object storing continuously updated partial / full residual	required
rng	RNG	`stochtree` object storing C++ random number generator to be used sampling algorithm	required
a	float	Shape parameter for the inverse gamma error variance model	required
b	float	Scale parameter for the inverse gamma error variance model	required

Name	Type	Description
	float	One draw from a Gibbs sampler for the error variance model, which depends on the rest of the model only through the “full” residual stored in a `Residual` object (net of predictions of any mean term such as a forest or an additive parametric fixed / random effect term).