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EM Algorithm

The Expectation-Maximization (EM) algorithm is an iterative optimization procedure for finding maximum likelihood or maximum a posteriori estimates of parameters in statistical models with latent variables or missing data. Introduced by Dempster, Laird, and Rubin in their landmark 1977 paper, EM alternates between computing the expected complete-data log-likelihood (E-step) and maximizing it with respect to the parameters (M-step), guaranteeing monotone non-decreasing likelihood at each iteration.

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Expectation-Maximization Algorithm

Rekod kaedah taksonomik · ml-model / statistics

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1–38. · DOI 10.1111/j.2517-6161.1977.tb01600.x

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Used in the same domainMaximum Likelihood Estimationmachine-suggested · Relational suggestion, not evidence.Used in the same domainMICEmachine-suggested · Relational suggestion, not evidence.