In statistics, an expectation–maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the …

The EM Algorithm The EM algorithm is used for obtaining maximum likelihood estimates of parameters when some of the data is missing. More generally, however, the EM algorithm can also be applied …

Sep 8, 2025 · The Expectation-Maximization (EM) algorithm is a powerful iterative optimization technique used to estimate unknown parameters in probabilistic models, particularly when the data is …

The algorithm iterates between the E-step and M-step until convergence. An easily readable summary of the basic theoretical properties of EM can be found in the entry on the Missing Information Principle, …

Next section introduces a simple version of EM, the K-means Algorithm.

The Algorithm The EM Algorithm is a numerical iterative for nding an MLE of . The rough idea is to start with an initial guess for and to use this and the observed data X to \complete" the data set by using X …

The EM stands for “Expectation-Maximization”, which indicates the two-step nature of the algorithm. At a high level, there are two steps: The “E-Step” and the “M-step” (duh!). The EM algorithm is not so …

Apr 19, 2025 · The Expectation–Maximization (EM) algorithm is a cornerstone of modern machine learning, providing a reliable framework to estimate parameters in models with unobserved (latent) …

Dec 12, 2025 · The expectation-maximization (EM) algorithm is a way to find maximum-likelihood estimates for model parameters when your data is incomplete, has missing data points, or has …

The Expectation-Maximization (EM) algorithm is a recursive algorithm that can be used to search for the maximum likelihood estimators of model parameters when the model includes some unobservable …