Description 
ALLFITDIST Fit all valid parametric probability distributions to data.
[D PD] = ALLFITDIST(DATA) fits all valid parametric probability distributions to the data in vector DATA, and returns a struct D of fitted distributions and parameters and a struct of objects PD representing the fitted distributions. PD is an object in a class derived from the ProbDist class.
[...] = ALLFITDIST(DATA,SORTBY) returns the struct of valid distributions sorted by the parameter SORTBY
NLogL  Negative of the log likelihood
BIC  Bayesian information criterion (default)
AIC  Akaike information criterion
AICc  AIC with a correction for finite sample sizes
[...] = ALLFITDIST(...,'DISCRETE') specifies it is a discrete distribution and does not attempt to fit a continuous distribution to the data
[...] = ALLFITDIST(...,'PDF') or (...,'CDF') plots either the PDF or CDF of a subset of the fitted distribution. The distributions are plotted in order of fit, according to SORTBY.
List of distributions it will try to fit
Continuous (default)
Beta
BirnbaumSaunders
Exponential
Extreme value
Gamma
Generalized extreme value
Generalized Pareto
Inverse Gaussian
Logistic
Loglogistic
Lognormal
Nakagami
Normal
Rayleigh
Rician
t locationscale
Weibull
Discrete ('DISCRETE')
Binomial
Negative binomial
Poisson
Optional inputs:
[...] = ALLFITDIST(...,'n',N,...)
For the 'binomial' distribution only:
'n' A positive integer specifying the N parameter (number of trials). Not allowed for other distributions. If 'n' is not given it is estimate by Method of Moments. If the estimated 'n' is negative then the maximum value of data will be used as the estimated value.
[...] = ALLFITDIST(...,'theta',THETA,...)
For the 'generalized pareto' distribution only:
'theta' The value of the THETA (threshold) parameter for the generalized Pareto distribution. Not allowed for other distributions. If 'theta' is not given it is estimated by the minimum value of the data.
Note: ALLFITDIST does not handle nonparametric kernelsmoothing, use FITDIST directly instead.
EXAMPLE 1
Given random data from an unknown continuous distribution, find the best distribution which fits that data, and plot the PDFs to compare graphically.
data = normrnd(5,3,1e4,1); %Assumed from unknown distribution
[D PD] = allfitdist(data,'PDF'); %Compute and plot results
D(1) %Show output from best fit
EXAMPLE 2
Given random data from a discrete unknown distribution, with frequency data, find the best discrete distribution which would fit that data, sorted by 'NLogL', and plot the PDFs to compare graphically.
data = nbinrnd(20,.3,1e4,1);
values=unique(data); freq=histc(data,values);
[D PD] = allfitdist(values,'NLogL','frequency',freq,'PDF','DISCRETE');
PD{1}
EXAMPLE 3
Although the Geometric Distribution is not listed, it is a special case of fitting the more general Negative Binomial Distribution. The parameter 'r' should be close to 1. Show by example.
data=geornd(.7,1e4,1); %Random from Geometric
[D PD]= allfitdist(data,'PDF','DISCRETE');
PD{1}
EXAMPLE 4
Compare the resulting distributions under two different assumptions of discrete data. The first, that it is known to be derived from a Binomial Distribution with known 'n'. The second, that it may be Binomial but 'n' is unknown and should be estimated. Note the second scenario may not yield a Binomial Distribution as the best fit, if 'n' is estimated incorrectly. (Best to run example a couple times to see effect)
data = binornd(10,.3,1e2,1);
[D1 PD1] = allfitdist(data,'n',10,'DISCRETE','PDF'); %Force binomial
[D2 PD2] = allfitdist(data,'DISCRETE','PDF'); %May be binomial
PD1{1}, PD2{1} %Compare distributions
