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STATISTICS
Statistical Algorithms
Expectation-Maximization (EM) Algorithm
An algorithm that solves puzzles by guessing missing pieces in data
14 days ago
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Why does the EM algorithm alternate between expectation and maximization steps when estimating parameters in models with latent variables?
Because it directly calculates the global maximum likelihood in one step without iteration.
Because it uses current parameter estimates to infer missing data distributions, then updates parameters to maximize likelihood based on those inferences.
Because it ignores latent variables and only focuses on observed data to estimate parameters.
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