Explore MATLAB source code curated for "最大似然估计" with clean implementations, documentation, and examples.
Implementing maximum likelihood estimation through an orthogonal projection generated subspace algorithm, with proven program execution results ready for reliable use
This algorithm collection provides fitting functions for multiple probability distributions, including Maximum Likelihood Estimation (MLE), Least Squares Estimation (LSE), and Expectation-Maximization (EM) algorithm-based Gaussian mixture model estimation. The package includes EM algorithm test cases with practical implementations and plotting functions for each distribution visualization. The implementation demonstrates parameter optimization techniques and distribution fitting workflows, making it highly valuable for statistical modeling and machine learning applications.
MATLAB implementation of Maximum Likelihood Estimation (MLE), a fundamental parameter estimation algorithm in statistics, featuring key probability distribution functions and optimization routines.
This document presents the Independent Component Analysis (ICA) algorithm, which is equivalent to Bell and Sejnowski's 1995 Infomax approach [1] formulated using maximum likelihood estimation. The implementation assumes no noise and requires the number of observations to equal the number of sources. Optimization is performed using the BFGS method [2], with dimensionality reduction via PCA and independent component count determination using Bayes Information Criterion (BIC) [3].
Course materials from data mining class covering EM algorithm implementation, maximum likelihood estimation principles, and cluster analysis techniques with code-related explanations
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.
MATLAB code example demonstrating maximum likelihood estimation for ARMA (Autoregressive-Moving Average) time series models with practical implementation details
Exponential estimation for generalized Gaussian probability distribution functions, including method of moments estimation and maximum likelihood estimation, with comparative analysis between these two approaches and implementation considerations.
Maximum Likelihood Estimation - Parameter Estimation Methodology with Implementation Approaches
MATLAB implementation of Maximum Likelihood Estimation (MLE) method with code examples and algorithm explanations