Expectation-Maximization (EM) algorithm is an iterative method for finding the maximum likelihood and maximum a posteriori estimates of parameters in models that typically depend on hidden variables.

While serving as a clustering technique, EM is also used in non-linear dimensionality reduction, missing value problems, and other areas.

For more complete information about compiler optimizations, see our Optimization Notice.
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