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

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



fits data to the probability of the form:

p(r) = sqrt(1/2/pi/sig^2)*exp(-((r-u)^2)/(2*sig^2))

with parameters: u,sig^2



format: result = fit_ML_normal( x,hAx )



input: x - vector, samples with 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

sig^2,u - fitted parameters

CRB_sig2,CRB_u - Cram?r-Rao Bound for the estimator value

RMS - RMS error of the estimation

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

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



　　 fits data to the probability of the form:

　　　　 p(r) = sqrt(1/2/pi/sig^2)*exp(-((r-u)^2)/(2*sig^2))

　　 with parameters: u,sig^2



　format:　 result = fit_ML_normal( x,hAx )



　input:　　x　 - vector, samples with 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

　　　　　　　　　　　 sig^2,u　　　　　- fitted parameters

　　　　　　　　　　　 CRB_sig2,CRB_u　 - Cram?r-Rao Bound for the estimator value

　　　　　　　　　　　 RMS　　　　　　　- RMS error of the estimation

　　　　　　　　　　　 type　　　　　　 -　ML