MATLAB Implementation of the EM Algorithm

This program implements a parameter estimation method based on the Expectation-Maximization (EM) algorithm. The algorithm achieves maximum likelihood estimation through iterative numerical solutions. The implementation alternates between two key computational steps: the E-step (Expectation) calculates the expected value of latent variables given current parameter estimates, while the M-step (Maximization) updates parameters to maximize the likelihood function based on these expected values. The EM algorithm is widely applied in data mining and machine learning domains due to its efficiency in solving practical problems with incomplete data. This MATLAB implementation provides a structured framework for handling such estimation tasks, featuring modular functions for probability calculations, convergence checking, and parameter updates. The code includes optimization for numerical stability and incorporates convergence criteria to ensure reliable parameter estimates.