EM (Expectation-Maximization) algorithm

The Expectation-Maximization (EM) algorithm is a statistical method used to find the best solution for problems with hidden or missing data. It works in two steps: the "Expectation" step estimates the missing data based on the current understanding of the model, while the "Maximization" step updates the model to fit the now complete data. The process repeats until the model stabilizes, finding parameters that best explain the data. It's commonly used in various fields, including machine learning and data analysis, for tasks like clustering and probabilistic modeling.