Concept
Concept
Expectation Maximization (EM) is an iterative algorithm used to find the maximum likelihood estimates of parameters in statistical models with latent variables. It alternates between estimating the expected value of the latent variables given the current parameters (E-step) and maximizing the likelihood by updating the parameters (M-step).
Concept
Expectation-Maximization (EM) is an iterative algorithm used for finding maximum likelihood estimates of parameters in statistical models, particularly when the data is incomplete or has hidden variables. It alternates between evaluating the expected value of the log-likelihood (E-step) and maximizing this expectation (M-step), improving parameter estimates iteratively until convergence.
Concept
Missing fields are like empty spots in a puzzle that make it hard to see the whole picture. They happen when some information isn't there, making it tricky to understand or use the information we have.
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