gmwm (version 2.0.0)

deriv_wn: Analytic D matrix white noise process

Description

Analytic D matrix white noise process

Usage

deriv_wn(tau)

Arguments

tau

A vec that contains the scales to be processed (e.g. 2^(1:J))

Value

A matrix with the first column containing the partial derivative with respect to $sigma[0]^2$.

Details

The haar wavelet variance is given as $nu^2(tau) = sigma_0^2 / tau$. Taking the derivative with respect to $sigma_0^2$ yields: $1/tau$

Examples

deriv_wn(2^(1:5))