EM Series

The EM Series refers to a sequence of mathematical methods used to estimate parameters in statistical models, especially when data is incomplete or uncertain. The "EM" stands for Expectation-Maximization, an iterative process where the algorithm alternates between estimating missing data (Expectation step) and optimizing parameters based on these estimates (Maximization step). This approach is widely applied in areas like machine learning, data analysis, and pattern recognition to find the best possible model fits even when dealing with complex or incomplete datasets.