Explore MATLAB source code curated for "极大似然估计" with clean implementations, documentation, and examples.
This source code implements Maximum Likelihood Estimation (MLE) for parameter estimation, featuring optimization algorithms and likelihood function implementations suitable for statistical modeling and machine learning applications.
MATLAB programming implementation of parameter estimation methods (including moment estimation and maximum likelihood estimation) and forecasting techniques (including difference equation method, inverse function method, and Green's function method) for stationary time series, providing convenient tools for practical applications with code examples and algorithm explanations.
Calculating maximum likelihood estimates for given data and corresponding probability density functions using MATLAB's numerical optimization tools
MATLAB simulation source code for model identification, featuring implementations of least squares modeling and maximum likelihood estimation modeling methods. Each key code example includes detailed explanations and algorithm descriptions.
Blind deconvolution restoration using maximum likelihood estimation with multi-frame image constraints achieves superior restoration outcomes through probabilistic optimization
Maximum Likelihood Estimation (MLE), also known as maximum probability estimation, is a theoretical point estimation method. Its fundamental principle is that after randomly drawing n sets of sample observations from a population model, the most reasonable parameter estimator should maximize the probability of obtaining these n sample observations from the model. Unlike least squares estimation which aims to find parameters that best fit sample data, MLE focuses on probability maximization. Implementation typically involves defining a likelihood function and using optimization algorithms to find parameter values that maximize this function.
This program implements the Expectation-Maximization (EM) algorithm, providing a numerical solution for parameter estimation using maximum likelihood techniques.
A MATLAB-implemented single target tracking algorithm program utilizing recursive algorithms, featuring Maximum Likelihood Estimation, Kalman Filter, Extended Kalman Filter, and Unscented Kalman Filter implementations with comprehensive comments for enhanced understanding.
This implementation includes both maximum likelihood estimation gradient algorithm and Fast ICA algorithm, which have been successfully debugged. Special attention should be paid to whether the input signal x is provided as a row vector or column vector during implementation and debugging.
MATLAB implementation of maximum likelihood estimation for wireless sensor network node localization, featuring signal strength-based position calculation and optimization algorithms.