how to fit gaussian model and plot it

hi i have vector of 1000 numbers i want to fit Gaussian model i use

[n,cent]=hist(x,50)
then

   bs = glmfit(cent,n,'normal');

then i want to plot the fit yfit = glmval(bs,cent_s); failed Error using glmval (line 64) At least three arguments are required. i want to plot

plot(cent_s,yfit)

Answer 1

[zepp]

You can do the following:

1) Estimate the mean and standard deviation using normfit

2) Calculate the probability estimates using normpdf

3) Plot the data and the estimates using plot

Example:

[m,s] = normfit(x);
y = normpdf(x,m,s);
plot(x,y,'.');

Answer 2

[Liam Walsh]

glmfit and glmval fit and evaluate generalized linear models, respectively. These can be used to model Gaussian data, but you need a set of predictors and responses (which are Gaussian).

If you have a single vector of Gaussian data that you want to fit to a Gaussian distribution, then there are two ways you can go about this. First, you can use normfit, normpdf, etc, as zepp shows in their answer.

Alternatively, you can make use of the fitdist function to create a NormalDistribution object, which has many convenience functions for visualizing the fit. To see all the options for working with the normal/Gaussian distribution that Statistics and Machine Learning Toolbox provides, please consult the following documentation page:

https://www.mathworks.com/help/stats/normal-distribution.html

% Create some sample data
rng(0, 'twister') % For reproducibility
x = normrnd(10, 2, 100, 1);

% Example 1: normfit, normpdf
[mu, sig] = normfit(x)

mu = 10.2462

sig = 2.3248

grid = 3:.1:17;
pdfvals = normpdf(grid, mu, sig);
plot(grid, pdfvals)

●

% Example 2: fitdist
normdist = fitdist(x, "normal") % Same mu, sigma values as the first example

normdist = 
  NormalDistribution

  Normal distribution
       mu = 10.2462   [9.78488, 10.7075]
    sigma =  2.3248   [2.04119, 2.70066]

plot(normdist)