Expectation Maximization

Expectation-Maximization (EM) is a statistical technique used to find the best estimates of hidden variables in data. Imagine you have a puzzle with some missing pieces; EM helps you guess those missing pieces (the hidden data) by making educated estimates based on what you know. It works in two steps: first, it uses current estimates to predict the missing pieces (the "Expectation" step), and then it updates the estimates based on these predictions (the "Maximization" step). This process repeats until the estimates stabilize, allowing for better understanding of complex data sets, especially when some information is incomplete or uncertain.