fit_ML_normal - Maximum Likelihood fit of the laplace distribution of i.i.d. samples!.

Given the samples of a laplace distribution, the PDF parameter is found



fits data to the probability of the form:

p(x) = 1/(2*b)*exp(-abs(x-u)/b)

with parameters: u,b



format: result = fit_ML_laplace( x,hAx )



input: x - vector, samples with laplace distribution to be parameterized

hAx - handle of an axis, on which the fitted distribution is plotted

if h is given empty, a figure is created.



output: result - structure with the fields

u,b - fitted parameters

CRB_b - Cram?r-Rao Bound for the estimator value

RMS - RMS error of the estimation

type - ML

-　fit_ML_normal - Maximum Likelihood fit of the laplace distribution of i.i.d. samples!.

　　　　　　　　　 Given the samples of a laplace distribution, the PDF parameter is found



　　 fits data to the probability of the form:

　　　　 p(x) = 1/(2*b)*exp(-abs(x-u)/b)

　　 with parameters: u,b



　format:　 result = fit_ML_laplace( x,hAx )



　input:　　x　 - vector, samples with laplace distribution to be parameterized

　　　　　　hAx - handle of an axis, on which the fitted distribution is plotted

　　　　　　　　　if h is given empty, a figure is created.



　output:　 result　- structure with the fields

　　　　　　　　　　　 u,b　　- fitted parameters

　　　　　　　　　　　 CRB_b　- Cram?r-Rao Bound for the estimator value

　　　　　　　　　　　 RMS　　- RMS error of the estimation

　　　　　　　　　　　 type　 -　ML