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 …

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, …

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

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 …

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. We begin our discussion with a very useful …

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, …

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 …

Feb 28, 2025 · In this tutorial, we’re going to explore Expectation-Maximization (EM) – a very popular technique for estimating parameters of probabilistic models and also the working horse behind …

5 days ago · 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 …