betavar: Variance of shinkage rule under beta prior.

In Alexestat/bayesShrink: Bayesian wavelet shrinkage

Description

Provides the variance of the wavelet shrinkage rule under beta prior.

Usage

betavar(theta, alpha, a, b, m, s)

Arguments

theta	Wavelet coefficients vector.
alpha	Weight of the point mass at zero function of the prior.
a	Shape parameter of the beta prior
b	Shape parameter of the beta prior
m	Upper value of the beta prior support.
s	Standard deviation of the normal random noise.

Value

Vector of variances of the shrinkage rule.

Examples

betavar(c(0,1,2),0.9,2,3,10,1)

Alexestat/bayesShrink documentation built on Oct. 6, 2020, 12:42 a.m.