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
horseshoespec
- 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.
