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

Given the samples of a log-normal distribution, the PDF parameter is found



fits data to the probability of the form:

p(x) = sqrt(1/(2*pi))/(s*x)*exp(- (log(x-m)^2)/(2*s^2))

with parameters: m,s



format: result = fit_ML_log_normal( x,hAx )



input: x - vector, samples with log-normal 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

m,s - fitted parameters

CRB_m,CRB_s - Cram?r-Rao Bound for the estimator value

RMS - RMS error of the estimation

type - ML - fit_ML_normal - Maximum Likelihood fit of the log-normal distribution of i.i.d. samples!.

　　　　　　　　　 Given the samples of a log-normal distribution, the PDF parameter is found



　　 fits data to the probability of the form:

　　　　 p(x) = sqrt(1/(2*pi))/(s*x)*exp(- (log(x-m)^2)/(2*s^2))

　　 with parameters: m,s



　format:　 result = fit_ML_log_normal( x,hAx )



　input:　　x　 - vector, samples with log-normal 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

　　　　　　　　　　　 m,s　　　　　- fitted parameters

　　　　　　　　　　　 CRB_m,CRB_s　- Cram?r-Rao Bound for the estimator value

　　　　　　　　　　　 RMS　　　　　- RMS error of the estimation

　　　　　　　　　　　 type　　　　 -　ML