EM algorithm

The Expectation-Maximization (EM) algorithm is a method used to find the best guesses for hidden or incomplete data in statistical models. It works in two steps: first, the "Expectation" step estimates the missing data based on current parameters, and then the "Maximization" step updates the model parameters to best fit both observed and estimated data. This process repeats until the model's guesses stabilize, allowing us to infer missing information and improve model accuracy systematically.