Gaussian Mixture Models

GMMs represent complex probability distributions as a weighted sum of simpler Gaussian distributions. Each component has its own mean and covariance matrix, along with a mixing coefficient. The model parameters are typically estimated using the Expectation-Maximization algorithm. GMMs can capture multi-modal distributions and handle varying cluster shapes.

Image segmentation, speaker identification, anomaly detection, clustering applications, and density estimation tasks.