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 …

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 …

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])

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 …

EM algorithm is an iteration algorithm containing two steps for each iteration, called E step and M step. The following gure illustrates the process of EM algorithm.

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 …

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 …

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 …

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) …