The expectation-maximization algorithm is a statistical technique used for finding maximum likelihood estimates of parameters in probabilistic models, particularly when the model depends on unobserved latent variables. It operates in two main steps: the expectation step (E-step), where the expected value of the log-likelihood is calculated given the current parameter estimates, and the maximization step (M-step), where parameters are updated to maximize this expected log-likelihood. This iterative process continues until convergence, allowing for effective estimation even with incomplete data.
congrats on reading the definition of Expectation-Maximization Algorithm. now let's actually learn it.