deriv_wn {simts}

Analytic D Matrix for a Gaussian White Noise (WN) Process

Description

Obtain the first derivative of the Gaussian White Noise (WN) process.

Usage

deriv_wn(tau)

Arguments

tau

A vec containing the scales e.g. 2^{\tau}

Value

A matrix with the first column containing the partial derivative with respect to \sigma^2.

Process Haar WV First Derivative

Taking the derivative with respect to \sigma^2 yields:

\frac{\partial }{{\partial {\sigma ^2}}}\nu _j^2\left( {{\sigma ^2}} \right) = \frac{1}{{{\tau _j}}}

Author(s)

James Joseph Balamuta (JJB)