Feb 4, 2025 · The Expectation-Maximization (EM) algorithm is an iterative method used in unsupervised machine learning to estimate unknown parameters in statistical models. It helps find the best values for unknown parameters, especially when some data is missing or hidden.

Sep 1, 2019 · Instead, we can use the expectation-maximization (EM) approach for finding the maximum likelihood estimates for the parameters θ. EM is a two-step iterative approach that starts from an...

Jan 19, 2022 · The Expectation-Maximisation (EM) Algorithm is a statistical Machine Learning method to find the maximum likelihood estimates of models with unknown latent variables. I am sure that that sentence will make no sense to some of you.

So the basic idea behind Expectation Maximization (EM) is simply to start with a guess for θ θ, then calculate z z, then update θ θ using this new value for z z, and repeat till convergence. The derivation below shows why the EM algorithm using this “alternating” updates actually works.

Python implementation of Expectation-Maximization algorithm (EM) for Gaussian Mixture Model (GMM). Code for GMM is in GMM.py. It's very well documented on how to use it on your data. For an example and visualization for 2D set of points, see the notebook EM_for_2D_GMM.ipynb.

Aug 28, 2020 · The Expectation-Maximization Algorithm, or EM algorithm for short, is an approach for maximum likelihood estimation in the presence of latent variables. A general technique for finding maximum likelihood estimators in latent variable models is the expectation-maximization (EM) algorithm.

Jan 29, 2025 · The Expectation-Maximization (EM) algorithm is a powerful iterative method used in statistics and machine learning for maximum likelihood estimation (MLE) in the presence of latent variables. Python offers several packages that implement the EM algorithm, making it accessible for data scientists and researchers to solve complex problems.

Oct 20, 2020 · We also see EM in action by solving step-by-step two problems with Python implementation (Gaussian mixture clustering and peppered moth population genetics). More importantly, we show that EM is not just a smart hack but has solid mathematical groundings on why it would work.

Feb 1, 2021 · In this article, we explored how to train Gaussian Mixture Models with the Expectation-Maximization Algorithm and implemented it in Python to solve unsupervised and semi-supervised learning problems. EM is a very useful method to find the maximum likelihood when the model depends on latent variables and therefore is frequently used in machine ...

Jul 30, 2022 · The EM algorithm is an iterative process that employs MLE in the presence of a latent variable. We have seen this algorithm at work in three different examples: K-Means (clustering), Two Coins...