EM Algorithm: Parameter Estimation with Maximum Likelihood for Incomplete Data
The EM algorithm, introduced by Dempster, Laird, and Rubin in 1977, solves maximum likelihood estimation problems specifically for incomplete datasets through an iterative expectation-maximization approach. This algorithm is particularly valuable for handling missing data scenarios where complete information is unavailable.