Home how do you generate random numbers from a bimodal Gaussian Probability Density Function in MATLAB?
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how do you generate random numbers from a bimodal Gaussian Probability Density Function in MATLAB?

user1068636 Published in 2017-12-07 06:02:18Z

How do you generate numbers from a bimodal Gaussian PDF in MATLAB?

For a unimodal symmetric Gaussian PDF it is really easy:


But now I want to draw 1000 numbers from a Gaussian PDF that has two peaks (or two means). How do I do this?

codeaviator Reply to 2017-12-07 13:35:45Z

You can generate the bimodal Normal (Gaussian) distribution by combining two Normal distributions with different mean and standard deviation (as explained in this comment).

In MATLAB you can do this in several ways:

First, we need to specify the mean (mu) and standard deviation (sigma) that characterize our Normal distributions, usually noted as N(mu, sigma).

Normal distribution a: N(-1, 0.5)

mu_a = -1;      % Mean (a).
sigma_a = 0.5;  % Standard deviation (a).

Normal distribution b: N(2, 1)

mu_b = 2;       % Mean (b).
sigma_b = 1;    % Standard deviation (b).

Finally, let's define the size of our random vectors:

sz = [1e4, 1];  % Size vector.

Now let's generate the bimodal random values by concatenating two vectors of Normally distributed random numbers:

OPTION 1: using randn

x_1 = [sigma_a*randn(sz) + mu_a, sigma_b*randn(sz) + mu_b];

OPTION 2: using normrnd

x_2 = [normrnd(mu_a, sigma_a, sz), normrnd(mu_b, sigma_b, sz)];

OPTION 3: using random

x_3 = [random('Normal', mu_a, sigma_a, sz), random('Normal', mu_b, sigma_b, sz)];

Let's visualize the results:

subplot(1, 3, 1); histogram(x_1);
subplot(1, 3, 2); histogram(x_2);
subplot(1, 3, 3); histogram(x_3);

Tommaso Belluzzo
Tommaso Belluzzo Reply to 2017-12-07 20:25:15Z

I propose you an alternative that uses gmdistribution:

% Means (X1 -> -0.5 and X2 -> 1.2)
m = [-0.5; 1.2];

% Standard Deviations (X1 -> 2.1 and X2 -> 1.3)
s = cat(3,2.1,1.3);

% Weights (X1 and X2 Equally Weighted 0.5)
w = ones(1,2) / 2;

% Create the Gaussian Mixture Model...
gmd = gmdistribution(m,s,w);

% Draw 1000 random numbers from it...
x = random(gmd,1000);

% Plot a histogram...
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