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 …

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 …

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 …

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 …

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

In order to solve this problem, we could use an iterative approach: first make a guess of the class label for each data point, then compute the means and update the guess of the class labels …

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.

Aug 28, 2020 · The expectation-maximization algorithm is an approach for performing maximum likelihood estimation in the presence of latent variables. It does this by first estimating the …

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

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 …