Expectation Maximization (EM) Algorithm

In this article, I present my own MATLAB implementation of the Expectation Maximization (EM) algorithm and provide comprehensive execution guidelines and program documentation. This write-up offers significant benefits for newcomers exploring this algorithm. Additionally, I share personal experiences and insights to facilitate deeper understanding of the algorithm's mechanics. First, I emphasize the importance of grasping the algorithm's core concepts, particularly the iterative two-step process involving expectation (E-step) calculations using probability distributions and maximization (M-step) parameter updates. Second, through practical experimentation, I've observed that varying initialization methods and parameter adjustments can yield substantially different convergence results and model performance. Finally, I strongly recommend hands-on experimentation with the implementation, as practical application remains crucial for mastering the algorithm's real-world usage. The code includes key functions for handling missing data and likelihood computation, demonstrating practical implementation strategies. Overall, I hope this article proves valuable for your learning journey and inspires further exploration of EM algorithm applications in statistical modeling and machine learning.