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 …

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 …

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 …

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 …

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 …

The Algorithm The EM Algorithm is a numerical iterative for nding an MLE of . The rough idea is to start with an initial guess for and to use this and the observed data X to \complete" the data …

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 …

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 …

The EM stands for “Expectation-Maximization”, which indicates the two-step nature of the algorithm. At a high level, there are two steps: The “E-Step” and the “M-step” (duh!). The EM …