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What is EM Algorithm Overview and Flowchart about?

The Expectation-Maximization (EM) algorithm is an iterative method used to find maximum likelihood or MAP estimates of parameters in statistical models with unobserved latent variables. It consists of two main steps: the Expectation Step (E-step), which estimates missing data based on observed data, and the Maximization Step (M-step), which updates model parameters to maximize data likelihood. The process repeats until convergence is achieved.

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22ct369

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EN