www.pudn.com > FitFunc.zip > readme.m

% 
% This folder contains a collection of "fitting" functions.  
% (Some has demo options - the third section) 
% The GENERAL input to the functions should be samples of the distribution. 
%  
% for example, if we are to fit a normal distribution ('gaussian') with a mean "u" and varaince "sig"^2 
% then the samples will distribute like:   
%      samples = randn(1,10000)*sig + u 
%  
%fitting with Least-Squares is done on the histogram of the samples. 
% fitting with Maximum likelihood is done directly on the samples.
%
% 
% Contents of this folder 
% ======================= 
% 1) Maximum likelihood estimators 
% 2) Least squares estimators 
% 3) EM algorithm for estimation of multivariant gaussian distribution (mixed gaussians) 
% 4) added folders: Create - which create samples for the EM algorithm test 
%                   Plot   - used to plot each of the distributions (parametric plot) 
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% Maximum likelihood estimators 
% ============================= 
% fit_ML_maxwell   - fit maxwellian distribution 
% fit_ML_rayleigh  - fit rayleigh distribution 
%                      (which is for example: sqrt(abs(randn)^2+abs(randn)^2)) 
% fit_ML_laplace   - fit laplace distribution 
% fit_ML_log_normal- fit log-normal distribution 
% fit_ML_normal    - fit normal (gaussian) distribution 
% 
% NOTE: all estimators are efficient estimators. for this reason, the distribution 
%       might be written in a different way, for example, the "Rayleigh" distribution 
%       is given with a parameter "s" and not "s^2". 
% 
% 
% least squares estimators 
% ========================= 
% fit_maxwell_pdf  - fits a given curve of a maxwellian distribution 
% fit_rayleigh_pdf - fits a given curve of a rayleigh distribution 
% 
% NOTE: these fit function are used on a histogram output which is like a sampled  
%       distribution function. the given curve MUST be normalized, since the estimator 
%       is trying to fit a normalized distribution function. 
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% Multivariant Gaussian distribution 
% ================================== 
% for demo of 1D mixed-gaussian fitting, run:  fit_mix_gaussian 
% for demo of 2D mixed-gaussian fitting, run:  fit_mix_2d_gaussian 
% 
% these routines fit and plot the results of the parameters of:  
% random distribution of random amount of gaussians with random parameters  
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