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deriv_wn

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:

d/dsigma2 nu[j]^2(sigma2) = 1/tau[j]

Author(s)

James Joseph Balamuta (JJB)

simts

Time Series Analysis Tools

v0.1.1
AGPL-3 | file LICENSE
Authors
Stéphane Guerrier [aut, cre, cph], James Balamuta [aut, cph], Roberto Molinari [aut, cph], Justin Lee [aut], Yuming Zhang [aut], Wenchao Yang [ctb], Nathanael Claussen [ctb], Yunxiang Zhang [ctb], Christian Gunning [cph], Romain Francois [cph], Ross Ihaka [cph], R Core Team [cph]
Initial release
2019-07-21

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