Expectation-Maximization (EM) Algorithm for Probabilistic Models
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Resource Overview
In statistical computing, the Expectation-Maximization (EM) algorithm is an iterative method for finding maximum likelihood (MLE) or maximum a posteriori (MAP) estimates of parameters in probabilistic models that depend on unobserved latent variables. The EM algorithm is widely used in machine learning and computer vision for data clustering applications. The algorithm alternates between an expectation step (E-step), which computes the expected value of the latent variables given current parameters, and a maximization step (M-step), which updates parameters to maximize the expected log-likelihood.
Detailed Documentation
In statistical computing, the Expectation-Maximization (EM) algorithm is an iterative method for obtaining maximum likelihood or maximum a posteriori estimates of parameters in probabilistic models that depend on unobserved latent variables. The EM algorithm is extensively applied in machine learning and computer vision for data clustering tasks to discover patterns and groupings within datasets. Through iterative expectation and maximization steps, the algorithm progressively refines parameter estimates, where the E-step computes posterior probabilities of latent variables using current parameters (typically implemented through Bayesian inference), and the M-step updates model parameters to maximize the expected complete-data log-likelihood (often solved via optimization techniques like gradient descent). This iterative process enhances estimation accuracy and consequently improves model performance and reliability.
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