Mini toolsDefinition
An iterative method for finding maximum likelihood estimates of parameters in statistical models with unobserved variables.
Detailed Explanation
The EM algorithm alternates between two steps: the Expectation step (E-step) computes the expected value of the log-likelihood function using current parameter estimates, and the Maximization step (M-step) updates parameters to maximize this expected log-likelihood. This process continues until convergence, handling incomplete data scenarios effectively.
Use Cases
Image reconstruction, natural language processing, computer vision, genetic mixture modeling, and clustering applications.
