Expectation-Maximization (EM) Algorithm

The Expectation-Maximization (EM) Algorithm is a method used to find the best parameters for a statistical model when some data are incomplete or missing. It works in two steps: the "Expectation" step estimates the missing data based on current model parameters, and the "Maximization" step updates the parameters to better fit the observed data. These steps repeat iteratively until the model accurately captures the data patterns. EM is useful in applications like clustering and data imputation, effectively handling uncertainty and improving model accuracy over time.