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 …

we simply assume that the latent data is missing and proceed to apply the EM algorithm. The EM algorithm has many applications throughout statistics. It is often used for example, in machine …

Jensen's Inequality The EM algorithm is derived from Jensen's inequality, so we review it here. = E[ g(E[X])

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, …

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 …

Jan 9, 2009 · The EM algorithm is an efficient iterative procedure to compute the Maximum Likelihood (ML) estimate in the presence of missing or hidden data. In ML estimation, we wish to estimate the …

In this set of notes, we give a broader view of the EM algorithm, and show how it can be applied to a large family of estimation problems with latent variables.

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 …

Jul 11, 2025 · The expectation-maximization (EM) algorithm is a widely-used optimization algorithm in machine learning and statistics. Its goal is to maximize the expected complete-data log-likelihood, …

Dec 19, 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 …