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 mixture distribution combines different component distributions with weights that typically sum to one (or can be renormalized). A gaussian-mixture 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) plot(xx,0.25*dnorm(xx,-2,1)+0.75*dnorm(xx,2,1),type="l",xlab="",ylab="")
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.