What are Gaussian Mixture Models?
Before we discuss Gaussian Mixture Models (GMMs), let's first understand what a Mixture Model is. A Mixture Model is a type of probability density model where it is assumed that the data is governed by several component distributions. If these distributions are Gaussian, then the model becomes a Gaussian Mixture Model. These component distributions are combined in order to provide a multi-modal density function, which becomes a mixture model.
Let's look at an example to understand how Mixture Models work. We want to model the shopping habits of all the people in South America. One way to do it would be to model the whole continent and fit everything into a single model, but people in different countries shop differently. We therefore need to understand how people in individual countries shop and how they behave.
To get a good representative model, we need to account for all the variations within the continent. In this case, we can use...