Gaussian Mixture Model Algorithm Implemented in MATLAB

This is a Gaussian Mixture Model (GMM) algorithm implemented in MATLAB, designed to estimate GMM parameters using the Expectation-Maximization (EM) algorithm. The implementation incorporates key statistical concepts including maximum likelihood estimation and Bayesian inference to perform cluster analysis on datasets. The algorithm's sample code demonstrates practical implementation approaches, featuring core components such as: - E-step: Posterior probability calculation using multivariate Gaussian probability density functions - M-step: Parameter updates through weighted mean and covariance computations - Convergence checking with log-likelihood improvement thresholds The included examples help users understand the algorithmic flow and can be used to validate and compare clustering performance. Users can modify parameters such as: - Number of Gaussian components (K) - Covariance matrix types (full, diagonal, spherical) - Initialization methods (K-means, random) - Convergence criteria settings The code structure allows for straightforward customization and optimization based on specific dataset characteristics to achieve improved clustering results. Key MATLAB functions employed include mvnpdf for probability calculations and eig for covariance matrix validation.