deriv_qn: Analytic D matrix quantisation noise process
In gmwm: Generalized Method of Wavelet Moments
Description
Usage
Arguments
Details
Value
Author(s)
Examples
Description
Analytic D matrix quantisation noise process
Usage
1
deriv_qn(tau)
Arguments
tau
A vec that contains the scales to be processed (e.g. 2^(1:J))
Details
The haar wavelet variance is given as nu^2(tau) = 3*Q[0]^2 / 2*tau^2.
Taking the derivative with respect to Q[0]^2 yields:
3/(2*tau^2)
.
The second derivative derivative with respect to Q[0]^2 is then:
0
.
Value
A matrix with the first column containing the partial derivative with respect to Q[0]^2.
Author(s)
JJB
Examples
1
gmwm documentation built on April 14, 2017, 4:38 p.m.
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Description Usage Arguments Details Value Author(s) Examples
Description
Analytic D matrix quantisation noise process
Usage
1 | deriv_qn(tau)
|
Arguments
tau |
A |
Details
The haar wavelet variance is given as nu^2(tau) = 3*Q[0]^2 / 2*tau^2. Taking the derivative with respect to Q[0]^2 yields:
3/(2*tau^2)
. The second derivative derivative with respect to Q[0]^2 is then:
0
.
Value
A matrix with the first column containing the partial derivative with respect to Q[0]^2.
Author(s)
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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