deriv_wn: Analytic D Matrix for a Gaussian White Noise (WN) Process
In SMAC-Group/gmwm: Generalized Method of Wavelet Moments
Description
Usage
Arguments
Value
Process Haar WV First Derivative
Author(s)
Examples
Description
Obtain the first derivative of the Gaussian White Noise (WN) process.
Usage
1
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
1
SMAC-Group/gmwm documentation built on Sept. 11, 2021, 10:06 a.m.
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Description Usage Arguments Value Process Haar WV First Derivative Author(s) Examples
Description
Obtain the first derivative of the Gaussian White Noise (WN) process.
Usage
1 | 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
1 |
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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