What is “mixture” in a gaussian mixture model

We often study Gaussian Mixture model as a useful model in machine learning and its applications.

What is the physical significance of this “Mixture“?

Is it used because a Gaussian Mixture Model models the probability of a number of random variables each with its own value of mean? If not, then what is the correct interpretation of this word.


A distribution combines different component distributions with weights that typically sum to one (or can be renormalized). A is the special case where the components are Gaussians.

For instance, here is a mixture of 25% N(2,1) and 75% N(2,1), which you could call “one part N(2,1) and three parts N(2,1)“:

xx <- seq(-5,5,by=.01)


Essentially, it’s like a recipe. Play around a little with the weights, the means and the variances to see what happens, or look at the two tags on CV.

Source : Link , Question Author : Upendra01 , Answer Author : Stephan Kolassa

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