Inferring Hidden Parameters from Incomplete Data: A Practical Guide to the EM Algorithm

The EM algorithm is fundamental for solving real-world problems where data contains hidden or unobserved variables. Understanding its mechanics through the intuitive coin-toss example equips practitioners with a powerful technique for parameter inference from incomplete data, a challenge ubiquitous in clustering, missing value imputation, and mixture modeling applications.