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Concept
Expectation-Maximization Algorithm
The Expectation-Maximization (EM) Algorithm is an iterative method used to find
maximum likelihood estimates
of parameters in
statistical models
with
latent variables
. It alternates between estimating the
expected value
of the
latent variables
(E-step) and maximizing the
likelihood function
given these expectations (M-step), improving the
parameter estimates
with each iteration until convergence.
Relevant Degrees
Probability and Statistics 70%
Artificial Intelligence Systems 30%
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