EM Algorithm (Expectation-Maximization)

The EM algorithm is a method used to find the best estimates of hidden or unknown data within models. It works in two steps: first, the Expectation step estimates the missing information based on current guesses; then, the Maximization step updates the model parameters to best fit all the data. Repeating these steps gradually improves the estimates, helping us uncover hidden patterns or complete incomplete data efficiently. It’s commonly used in statistical learning when some data is incomplete or ambiguous.