# What is the difference between a mixture model and a multimodal distribution?

A distribution that is a "Mixture model" has a very similar definition as a "multimodal" distribution.

Wikipedia Says:

a multimodal distribution is a continuous probability distribution with two or more mode

Now to compare:

In statistics, a mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring that an observed data set should identify the sub-population to which an individual observation belongs.

What is the difference between the two? They sound like they are the same thing!

To complement @matt's answer, you can also consider the beta distribution with $\alpha = .5$ and $\beta = .5$. It is illustrated by the red line in the figure below (copied from Wikipedia). As you can see, it is multimodal (viz., bimodal), but it isn't a mixture distribution: