EM Method

The Expectation-Maximization (EM) method is an iterative algorithm used to find hidden patterns in data with missing or incomplete information. It works in two steps: the "Expectation" step estimates the missing data based on current parameters, and the "Maximization" step updates these parameters to better fit the observed data. Repeating these steps improves the model's accuracy. EM is commonly used in statistical modeling, such as clustering or pattern recognition, helping to uncover structure in complex data sets where some information is incomplete or uncertain.