Explore MATLAB source code curated for "EM算法" with clean implementations, documentation, and examples.
Course materials from data mining class covering EM algorithm implementation, maximum likelihood estimation principles, and cluster analysis techniques with code-related explanations
MATLAB implementation of the EM (Expectation Maximization) algorithm designed for pattern classification, featuring probabilistic modeling and iterative optimization
This algorithm implementation includes maximum likelihood estimation, least squares estimation, EM algorithm-based Gaussian mixture model estimation with test cases, and plotting functions for each distribution. Features comprehensive code examples demonstrating parameter optimization techniques and expectation-maximization workflows.
Implementation of image segmentation using the EM algorithm with working code. The code is concise and clear, making it ideal for beginners to understand probabilistic clustering approaches in computer vision.
A MATLAB program implementing the Expectation-Maximization (EM) algorithm with specific applications in image processing tasks, including parameter estimation and probabilistic modeling
The EM algorithm, introduced by Dempster, Laird, and Rubin in 1977, solves maximum likelihood estimation problems specifically for incomplete datasets through an iterative expectation-maximization approach. This algorithm is particularly valuable for handling missing data scenarios where complete information is unavailable.
A fundamental introduction to the EM (Expectation-Maximization) algorithm with practical implementation insights for parameter estimation in probabilistic models containing latent variables.
Implementation of the Expectation-Maximization algorithm for estimating k-dimensional Gaussian mixture models. The algorithm accepts input data matrix X(n,d) with n observations and d-dimensional variables, maximum Gaussian components k, likelihood tolerance ltol, maximum iterations maxiter, plotting flag pflag, and initial parameter structure for weights, means, and covariances. Returns estimated mixture parameters and log-likelihood value.