The EM Algorithm in Practice: A Methodological Survey of Applications, Extensions, and Computation

Abstract

This paper presents the fundamental principles of the Expectation-Maximization (EM) algorithm, a well-established iterative method for parameter estimation in statistical models involving latent variables. A representative dataset is selected to illustrate the practical application of the algorithm. Implementations of the EM algorithm are developed in both C and Python programming languages, enabling a comparative assessment of computational performance and accessibility. The corresponding experimental results are systematically presented and analyzed, with particular emphasis on the algorithm’s convergence behavior and the accuracy of parameter estimates. The findings provide insights into the practical utility and robustness of the EM algorithm across different programming environments.