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deriv_qn

Analytic D matrix for Quantization Noise (QN) Process

Description

Obtain the first derivative of the Quantization Noise (QN) process.

Usage

deriv_qn(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 Q^2.

Process Haar WV First Derivative

Taking the derivative with respect to Q^2 yields:

d/dQ2 nu[j]^2 (Q2) = (6) / (tau[j]^2)

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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