How do I generate a random number between two numbers with using a distribution
2 views (last 30 days)
Show older comments
Emre Can Yilmaz
on 22 Mar 2022
Commented: Emre Can Yilmaz
on 22 Mar 2022
I I know that we can define distribution in the random command, but the random numbers I generate with the random command are integers. I'm using the rand command for decimal random numbers and this time I can't define a distribution. In short, how can I create a normal distribution or triangular distribution of 100 random numbers consisting of decimal numbers?
0 Comments
Accepted Answer
Bruno Luong
on 22 Mar 2022
% define the siscrete value and their relative pdf
xval=(-10:10);
p=exp(-(xval/5).^2); % Gaussian function, triangular whatever you like
n = 1e6; % number of samples
c=cumsum(p);
c=c/c(end);
xrand=xval(discretize(rand(1,n ),[0; c(:)])); % here is the random
% Check graphically
p = p/sum(p);
figure
subplot(2,1,1);
plot(xval,p)
subplot(2,1,2);
histogram(xrand)
3 Comments
Bruno Luong
on 22 Mar 2022
If you want continuous distribution (and not discrete, I don't know what means "decimal" and the computer is the same as me)
% define the siscrete value and their relative pdf
xval=(-10:10);
xmid = (xval(1:end-1) + xval(2:end))/2;
p=exp(-((xmid-3)/5).^2); % Gaussian function, triangular whatever you like
n = 1e6; % number of samples
c=cumsum(p);
c=c/c(end);
xrand=interp1([0 c], xval, rand(1,n));
% Check graphically
p = p/sum(p);
figure
ax1=subplot(2,1,1);
plot(xmid,p)
ax2=subplot(2,1,2);
histogram(xrand,100)
linkaxes([ax1 ax2], 'x')
More Answers (1)
Paul
on 22 Mar 2022
Emre,
The statement that the the random() command only generates integers doesn't sound correct. The doc page shows an an example of generating random numbers from a Weibull diistribution which are not integers. Can you post an example that illustrates the behavior?
For a standard normal distribution you can use randn(), and then adjust the outputs for whatever mean and variance is desired. Or use normrnd() in the Statistics and Machine Learning Toolbox, or other options in that toolbox. That toolbox supports many distributions, including the Triangular distribution, for which you can get the pdf, cdf, random numbers, and lots of other interesting things.
2 Comments
Paul
on 22 Mar 2022
Here's an example using Stats Toolbox functionality
pd = makedist('Triangular',0,20,100)
rng(100);
v = random(pd,100000,1);
histogram(v,'Normalization','pdf')
See Also
Products
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!