deriv_wn: Analytic D Matrix for a Gaussian White Noise (WN) Process
In schoi355/gmwm: Generalized Method of Wavelet Moments
deriv_wn R Documentation
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)
Examples
deriv_wn(2^(1:5))
schoi355/gmwm documentation built on April 11, 2022, 1:21 a.m.
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| deriv_wn | R Documentation |
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 |
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)
Examples
deriv_wn(2^(1:5))
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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