Difference between revisions of "Expectation Maximization"
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| − | Expectation Maximization (EM) is an iterative [[ | + | Expectation Maximization (EM) is an iterative [[category::method]] for maximum likelihood estimation of the parameters of a statistical model. |
<!-- a technique to infer the parameters of a statistical model and the underlying intuition is that an optimal parametrized statistical model will give a highest probability to the training data on which it is trained. It consists of the expectation or E- step in which the log likelihood probability is calculated based on the current estimate of the parameters and the hidden (latent) variables and a maximization or M-step in which the value of the parameters is updated to increase the maximum value of the likelihood function. EM can be used to discover a local maxima for the log-likelihood function. | <!-- a technique to infer the parameters of a statistical model and the underlying intuition is that an optimal parametrized statistical model will give a highest probability to the training data on which it is trained. It consists of the expectation or E- step in which the log likelihood probability is calculated based on the current estimate of the parameters and the hidden (latent) variables and a maximization or M-step in which the value of the parameters is updated to increase the maximum value of the likelihood function. EM can be used to discover a local maxima for the log-likelihood function. | ||
Revision as of 11:01, 22 September 2011
Expectation Maximization (EM) is an iterative method for maximum likelihood estimation of the parameters of a statistical model.
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