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 …

The EM algorithm can fail due to singularity of the log-likelihood function. For example, when learning a GMM with 10 components, the algorithm may decide that the most likely solution is for one of the …

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

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.

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 …

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 values for …

Jun 18, 2025 · The Expectation-Maximization (EM) algorithm is a cornerstone of machine learning, enabling the estimation of model parameters in the presence of incomplete or missing data. This …

The EM Algorithm lecture from MIT delves into the Expectation-Maximization algorithm, explaining its methodology and applications in statistical computation and machine learning.