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Expectation–maximization (EM) algorithm

Expectation–maximization (EM) algorithm. In statistics, an expectation–maximization (EM) algorithm is an iterative method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables.

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Expectation–maximization (EM) algorithm

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  1. Expectation–maximization (EM) algorithm • In statistics, an expectation–maximization (EM) algorithm is an iterative method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. • The EM iteration alternates between performing an expectation (E) step, which computes the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step. From Wikipedia

  2. Expectation–maximization (EM) algorithm (cont.) From Wikipedia

  3. Expectation–maximization (EM) algorithm (cont.) From Wikipedia

  4. Expectation–maximization (EM) algorithm (cont.) From Wikipedia

  5. Introduction and Terminology

  6. Enumeration Procedure

  7. The algorithm

  8. Further reading

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