Gaussian Mixture Models (GMMs): from Theory to Implementation

In-depth explanation of GMMs and the Expectation-Maximization algorithm used to train them

Gaussian Mixture Models (GMMs) are statistical models that represent the data as a mixture of Gaussian (normal) distributions. These models can be used to identify groups within the dataset, and to capture the complex, multi-modal structure of data distributions.

GMMs are used in a variety of machine learning applications, including clustering, density…