set_horseshoe {bvhar} | R Documentation |
Horseshoe Prior Specification
Description
Set initial hyperparameters and parameter before starting Gibbs sampler for Horseshoe prior.
Usage
set_horseshoe(local_sparsity = 1, group_sparsity = 1, global_sparsity = 1)
## S3 method for class 'horseshoespec'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
is.horseshoespec(x)
## S3 method for class 'horseshoespec'
knit_print(x, ...)
Arguments
local_sparsity |
Initial local shrinkage hyperparameters |
group_sparsity |
Initial group shrinkage hyperparameters |
global_sparsity |
Initial global shrinkage hyperparameter |
x |
Any object |
digits |
digit option to print |
... |
not used |
Details
Set horseshoe prior initialization for VAR family.
-
local_sparsity
: Initial local shrinkage -
group_sparsity
: Initial group shrinkage -
global_sparsity
: Initial global shrinkage
In this package, horseshoe prior model is estimated by Gibbs sampling, initial means initial values for that gibbs sampler.
References
Carvalho, C. M., Polson, N. G., & Scott, J. G. (2010). The horseshoe estimator for sparse signals. Biometrika, 97(2), 465-480.
Makalic, E., & Schmidt, D. F. (2016). A Simple Sampler for the Horseshoe Estimator. IEEE Signal Processing Letters, 23(1), 179-182.