fit_maxwell_pdf - Non Linear Least Squares fit of the maxwellian distribution.

given the samples of the histogram of the samples, finds the

distribution parameter that fits the histogram samples.



fits data to the probability of the form:

p(r) = sqrt(2/pi)*(a^(-3/2))*(r^2)*exp(-(r^2)/(2*a))

with parameter: a

format: result = fit_maxwell_pdf( x,y,W,hAx )

input: y - vector, samples of the histogram to be fitted

x - vector, position of the samples of the histogram (i.e. y = f(x,a))

W - matrix or scalar, a square weighting matrix of the size NxN where

N = length(y), or 0 to indicate no weighting is needed.

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

a - fitted parameter

VAR - variance of the estimation

type- weighted LS or not weighted LS

iter- number of iteration for the solution-　fit_maxwell_pdf - Non Linear Least Squares fit of the maxwellian distribution.

given the samples of the histogram of the samples, finds the

distribution parameter that fits the histogram samples.



fits data to the probability of the form:

p(r) = sqrt(2/pi)*(a^(-3/2))*(r^2)*exp(-(r^2)/(2*a))

with parameter: a

format:　 result = fit_maxwell_pdf( x,y,W,hAx )

input:　　y　 - vector, samples of the histogram to be fitted

x　 - vector, position of the samples of the histogram (i.e. y = f(x,a))

W　 - matrix or scalar, a square weighting matrix of the size NxN where

N = length(y), or 0 to indicate no weighting is needed.

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

a　 - fitted parameter

VAR - variance of the estimation

type- weighted LS or not weighted LS

iter- number of iteration for the solution