MATLAB Implementation of Gaussian Mixture Model with Source Code

This repository provides MATLAB source code for implementing Gaussian Mixture Models (GMM). GMM serves as a fundamental statistical model with extensive applications across multiple domains, especially prominent in signal processing systems. The implementation demonstrates key components such as initialization methods for Gaussian parameters, iterative EM algorithm for maximizing likelihood estimation, and covariance matrix handling for multivariate data. Beyond signal processing applications, Gaussian Mixture Models prove highly valuable in data mining tasks for clustering and density estimation, as well as in pattern recognition systems for classification problems. The code structure includes modular functions for component responsibility calculations, parameter updates, and convergence checking, making it suitable for educational purposes and practical implementations. This implementation offers beginners a comprehensive reference for understanding GMM's theoretical foundations through practical MATLAB coding examples, featuring proper matrix operations for efficient computation and visualization capabilities for result analysis. The code emphasizes numerical stability through techniques like logarithm-based probability calculations and regularization methods for covariance matrices.