deriv_wn: Analytic D Matrix for a Gaussian White Noise (WN) Process
In SMAC-Group/simts: Time Series Analysis Tools
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:
\frac{\partial }{{\partial {\sigma ^2}}}\nu _j^2\left( {{\sigma ^2}} \right) = \frac{1}{{{\tau _j}}}
Author(s)
James Joseph Balamuta (JJB)
SMAC-Group/simts documentation built on Sept. 4, 2023, 5:25 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:
\frac{\partial }{{\partial {\sigma ^2}}}\nu _j^2\left( {{\sigma ^2}} \right) = \frac{1}{{{\tau _j}}}
Author(s)
James Joseph Balamuta (JJB)
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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