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The Expectation-Maximization (EM) algorithm is defined as the combination of various unsupervised machine learning algorithms, which is used to determine the local maximum likelihood estimates (MLE) or maximum a posteriori estimates (MAP) for unobservable variables in statistical models.
Aug 1, 2023 · The Expectation-Maximization (EM) algorithm is an iterative optimization method that combines different unsupervised machine learning algorithms to find maximum likelihood or maximum posterior estimates of parameters in statistical models that involve unobserved latent variables.
The EM algorithm is used to find (local) maximum likelihood parameters of a statistical model in cases where the equations cannot be solved directly. Typically these models involve latent variables in addition to unknown parameters and known data observations.
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.
Jul 30, 2022 · The EM algorithm is an iterative method of statistical analysis that employs MLE in the presence of latent variables. It can be broken down into two major steps (Fig. 1): the expectation step...
in this example we would like to derive the EM algorithm and see if the EM algorithm would match with our intuition. Solution: To start the EM algorithm, we first need to specify the missing data and the complete data.
May 13, 2020 · Expectation-maximization (EM) is a popular algorithm for performing maximum-likelihood estimation of the parameters in a latent variable model. In this post, I discuss the theory behind, and intuition into this algorithm.
Feb 13, 2024 · Learn the principles and steps of the Expectation-Maximization (EM) algorithm. Explore the advantages and disadvantages of the EM algorithm in parameter estimation and missing data handling. Discover the applications of the EM algorithm in various domains such as natural language processing, image reconstruction, and model parameter estimation.
The EM algorithm. In the previous set of notes, we talked about the EM algorithm as applied to fitting a mixture of Gaussians. 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.
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 model parameter(s) for which the observed data are the most likely. Each iteration of the EM algorithm consists of two processes: The E-step, and the M-step.
