Gaussian Mixture Models

Gaussian Mixture Models (GMMs) are probabilistic models that assume all data points are generated from a mixture of several Gaussian distributions with unknown parameters. They are commonly used for clustering, density estimation, and anomaly detection in various fields such as finance, biology, and image processing. A GMM is characterized by its parameters, including the mean and covariance of each Gaussian component, as well as the weights that represent the proportion of each component in the mixture. The Expectation-Maximization (EM) algorithm is typically employed for fitting GMMs to data, making it a powerful tool for unsupervised learning tasks.