GMM - Well-Performing Code with Demo

This text details the advantages and features of the GMM code implementation. GMM stands for Gaussian Mixture Model, a widely-used probabilistic model in machine learning and statistics. The implementation employs Expectation-Maximization (EM) algorithm for parameter estimation, making it particularly effective for high-dimensional data modeling in fields like acoustics, image processing, and text analysis. Compared to other models, this GMM implementation demonstrates superior performance through efficient covariance matrix handling and component validation mechanisms. The code includes key functions for model initialization, likelihood computation, and cluster assignment with optimized numerical stability. Furthermore, the package contains a comprehensive demo that visually demonstrates GMM's working principles through probability density visualization and cluster separation examples. This makes the code an excellent choice for Gaussian mixture modeling tasks, especially for researchers needing clear implementation insights and immediate practical validation.