MATLAB Implementation of Expectation-Maximization (EM) Algorithm
Application Context: In statistical computing, the Expectation-Maximization (EM) algorithm is used to find maximum likelihood or maximum a posteriori estimates of parameters in probabilistic models that depend on unobserved latent variables. The EM algorithm is frequently applied in machine learning and computer vision for data clustering tasks. Key Technology: The EM algorithm iterates through two alternating steps: - E-step (Expectation): Computes the expected value of the log-likelihood function using current estimates of hidden variables - M-step (Maximization): Finds parameters that maximize the expected log-likelihood computed in the E-step Parameters estimated in the M-step are reused in the next E-step, creating an iterative convergence process.