Explore MATLAB source code curated for "最大期望算法" with clean implementations, documentation, and examples.
A parameter estimation model for Gaussian mixture distributions using the Expectation-Maximization algorithm, applicable in communication systems, signal processing, and statistical analysis with implementation-ready algorithmic frameworks.
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.
The EM (Expectation-Maximization) algorithm for estimating unknown data is widely applicable and can be used for synchronization tasks, among other implementations involving iterative probability estimation.
The EM algorithm, also known as the Expectation-Maximization algorithm, is an iterative parameter estimation method used for optimizing likelihood functions in statistical models.
MATLAB implementation of Expectation-Maximization (EM) algorithm - an iterative method for finding maximum likelihood or maximum a posteriori estimates of parameters in statistical models with unobserved latent variables, featuring probability model handling and parameter optimization.
The EM Algorithm: An Iterative Optimization Technique for Parameter Estimation with Code Implementation Insights
EM Algorithm Implementation for Parameter Estimation with Missing or Latent Variables