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

Wikipedia Says:

a

multimodal distributionis a continuous probability distribution with two or more mode

Now to compare:

In statistics, a

mixture modelis 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!

**Contents**hide

#### Best Answer

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:

### Similar Posts:

- Solved – Why are all known distributions unimodal
- Solved – Counterexamples where Median is outside [Mode-Mean]
- Solved – How are the numbers of modes of marginal and joint distributions related
- Solved – Is this distribution bimodal?
- Solved – Distribution of “p-value-like” quantities under null hypothesis