Method mfh/fisher


  FISHER.M Calculation of the fisher Information Matrix/Covariance
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
  INPUTS:
          - The MFH object of the time series of the signals.
          - A plist object.
 
  OUTPUTS:
          - Covariance matrix of the parameters. Unlike the 
            ssm/fisher and matrix/fisher, this functions returns
            directly the covariance matrix of the parameters. This
            is due to the properties of the MATLAB function handle 
            objects.
 
 
  EXAMPLE:  C = fisher(mfh_object, plist);
 
  Parameters Description
 
  NK 2014
 
Method Details
Access public
Defining Class mfh
Sealed 0
Static 0

Parameter Description

Default

no description
Key Default Value Options Description
fisher
P0 '' none The numerical values of the parameters.
PINV 1
  • 1
  • 0
Use the Penrose-Moore pseudoinverse
TOL [] none Tolerance for the Penrose-Moore pseudoinverse
DIFFSTEP [] none Numerical differentiation step for ssm models
NOISE [] none The noise time series or MFH objects.
REGULARIZE 0
  • 0
  • 1
If the resulting fisher matrix is not positive definite, try a numerical trick to continue sampling.
YUNITS 'm s^-2' none The Y units of the noise time series, in case the MFH object is a 'core' type.
NEARESTSPD 0
  • 0
  • 1
Try to find the nearest symmetric and positive definite covariance matrix, with the 'nearestSPD' method from MATLAB file exchange.
FREQUENCIES [] none The frequency range.
NAME {} [0x0] none The name of the function handles.
TS FH [] none The time series function handles to perform the FFT. Must be in an array.
TRIM [100 -100] none A 2x1 vector that denotes the samples to split from the star and end of the time-series (split in offsets).
WIN 'BH92' none The window to apply to the data.
FS 1 none For the cae of 'CORE', the sampling frequency of the time series is needed.
TRANSFORM, TRANSFORMATIONS {} [0x0] none A list of transformations to be applied to the inputs before evaluating the expression.
BIN GROUPS [] none A numerical vector that denotes to the start and end frequency value that corresponds to the given frequency block amplitude. For more information, please type >> doc mfh_model_loglikelihood.
S [] none The inverse cross-spectrum matrix. Used fro the case of the student-t likelihood.
ERROR RATIO 0.5 none The percentage of knowldge level of each frequency bin. Used fro the case of the student-t likelihood
NU 'common'
  • 'common'
  • 'by bins groups'
  • 'by bins'
The 'degrees of freedom' parameter for the student-t distribution, as proposed in PRD 84, 122004 (2011). There are three posibilities to ad th nu coefficient. The 'COMMON' choise, follows the simplified logic that the noise frequency bins follow the same distribution. The second 'BY BIN GROUPS' considers common value for the coefficient for neighboring frequency bins, defined with the key 'BIN GROUPS'. The last choice considers a dofferent degree of freedom for each frequency bin.
VERSION 'core'
  • 'core'
  • 'ao'
Choose between 'AO' and 'CORE' types.
computeICSMatrix
INPUT '' none The injection signals.
NOUT '' none The number of outputs.
INVERSE 1
  • 1
  • 0
Set to false to return the spectrum matrix, but not inverted.
ISDIAG 0
  • 0
  • 1
For the case of systems where the cross-spectrum matrix is diagonal it can be set to true to skip estimating the non-diagonal elements. Useful for multiple inputs/outputs.
INTERPOLATION METHOD, INTERP METHOD 'LINEAR'
  • 'LINEAR'
  • 'SPLINE'
  • 'PCHIP'
  • 'CUBIC'
The interpolation method.
NAVS 5 none The number of averages to use when calculating PSD and CPSD.
FREQS [] none Array of frequencies where the analysis is performed.
NOISE SCALE 'PSD'
  • 'PSD'
  • 'LPSD'
Select the way to handle the noise/weight data. Can use the PSD/CPSD or the LPSD/CLPSD functions.
BIN DATA 0
  • 0
  • 1
Set to true to re-bin the measured noise data.
OLAP -1 none The segment percent overlap [-1 == take from window function]
ORDER 1 none The order of segment detrending during PSD. For more info type 'doc ao.psd'.
FIT NOISE MODEL 0
  • 0
  • 1
Set to true to attempt a fit on the noise spectra using the 'polyfitSpectrum' function.
POLYNOMIAL ORDER [-4 -3 -2 -1 0 1 2 3 4] none The order of the polynomial to be used in the 'polyfitSpectrum' function.
PLOT FITS 0
  • 0
  • 1
Set to true to produce plots of the fits in case of 'fit noise model' is set to true.
lpsd
PSLL 200 none The peak sidelobe level for Kaiser windows.
Note: it is ignored for all other windows
DROP WINDOW SAMPLES 1
  • 1
  • 0
Drop the recommended (by the window) number of samples of the final computed spectral series.
KDES 100 none The desired number of averages.
JDES 1000 none The desired number of spectral frequencies to compute.
LMIN 0 none The minimum segment length.
SCALE 'PSD'
  • 'PSD'
  • 'ASD'
  • 'PS'
  • 'AS'
The scaling of output. Choose from:
  • PSD - Power Spectral Density
  • ASD - Amplitude (linear) Spectral Density
  • PS - Power Spectrum
  • AS - Amplitude (linear) Spectrum
psd
NFFT -1 none The number of samples in each fft [default: length of input data].
A string value containing the variable 'fs' can also be used, e.g.,
plist('Nfft', '2*fs')

Example

plist('P0', '', 'PINV', [true], 'TOL', [[]], 'DIFFSTEP', [[]], 'NOISE', [[]], 'REGULARIZE', [false], 'YUNITS', 'm s^-2', 'NEARESTSPD', [false], 'FREQUENCIES', [[]], 'NAME', cell(0,0), 'TS FH', [[]], 'TRIM', [[100 -100]], 'WIN', 'BH92', 'FS', [1], 'TRANSFORM', cell(0,0), 'BIN GROUPS', [[]], 'S', [[]], 'ERROR RATIO', [0.5], 'NU', 'common', 'INPUT', '', 'NOUT', '', 'INVERSE', [true], 'ISDIAG', [false], 'INTERPOLATION METHOD', 'LINEAR', 'NAVS', [5], 'FREQS', [[]], 'NOISE SCALE', 'PSD', 'BIN DATA', [false], 'OLAP', [-1], 'ORDER', [1], 'FIT NOISE MODEL', [false], 'POLYNOMIAL ORDER', [[-4 -3 -2 -1 0 1 2 3 4]], 'PLOT FITS', [false], 'PSLL', [200], 'DROP WINDOW SAMPLES', [true], 'KDES', [100], 'JDES', [1000], 'LMIN', [0], 'SCALE', 'PSD', 'NFFT', [-1], 'VERSION', 'core')

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Some information of the method mfh/fisher are listed below:
Class name mfh
Method name fisher
Category Signal Processing
Package name ltpda
VCS Version 967b0eec0dece803a81af8ef54ad2f8c784b20b2
Min input args 1
Max input args -1
Min output args 1
Max output args -1
Can be used as modifier 1
Supported numeric types {'double'}




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