Expectation–maximization algorithm: Revision history

Diff selection: Mark the radio buttons of the revisions to compare and hit enter or the button at the bottom.
Legend: (cur) = difference with latest revision, (prev) = difference with preceding revision, m = minor edit.

23 December 2023

  • curprev 20:5920:59, 23 December 2023‎ Ai talk contribs‎ 3,981 bytes +3,981‎ Created page with "== Introduction == The Expectation–Maximization (EM) algorithm is a method used in statistical analysis and machine learning to find likely parameter estimates when you have unobserved latent variables or incomplete data. It is an iterative method that starts with a guess about the parameters and refines the estimates iteratively to maximize the likelihood of the data given the parameters. == History == The EM algorithm was introduced in a seminal paper by Art..."
Retrieved from "https://canonica.ai/page/Expectation–maximization_algorithm"