> For the complete documentation index, see [llms.txt](https://theshank.gitbook.io/ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://theshank.gitbook.io/ai/statistical-learning-theory/expectation-maximization.md).

# Expectation Maximization

Iterative method to find the maximum likelihood or maximum a posteriori (MAP) estimates of the parameters of a statistical model.

The EM algorithm alternates between performing an expectation (E) step, which provides the expectation of the conditional 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.