Explore MATLAB source code curated for "最大似然估计" with clean implementations, documentation, and examples.
MATLAB implementation for estimating GARCH(1,1) model parameters using Maximum Likelihood Estimation (MLE). Includes simulation-based validation testing for Type I and Type II errors (size and power tests) to ensure correct model specification and statistical reliability.
1. Master maximum likelihood estimation for multivariate normal distributions through hands-on implementation; 2. Understand Bayesian classification with minimum error rate under multivariate normal distributions; 3. Gain deeper insights into other parameter estimation techniques with practical code examples.
Maximum Likelihood Estimation - Achieving Highly Accurate Parameter Estimates with Optimal Speed
This resource provides comprehensive coverage of the alternating projection algorithm for maximum likelihood estimation, including detailed MATLAB simulation source code with implementation insights and algorithmic explanations.
Maximum likelihood estimation-based independent component analysis algorithms with three implementations: stochastic gradient algorithm, relative gradient algorithm, and fast fixed-point algorithm, including detailed code implementation approaches and optimization techniques.
In face recognition using sparse representation methods, the fidelity of sparse representation is typically expressed as the L2 norm of the residual. Maximum likelihood estimation theory demonstrates that this formulation requires residuals to follow a Gaussian distribution, an assumption that often fails in practical scenarios, particularly when test images contain abnormal pixels from noise, occlusion, or disguise. This limitation reduces the robustness of traditional sparse representation models built on conventional fidelity expressions. The maximum likelihood sparse representation recognition model addresses this by reformulating the fidelity expression as a maximum likelihood distribution function for residuals, transforming the maximum likelihood problem into a weighted optimization framework with enhanced robustness against abnormal pixels.
Signal Estimation, Maximum Likelihood Estimation Module, and Cramér-Rao Bound Module - Technical Overview with Implementation Insights
Expectation-Maximization Algorithm for Maximum Likelihood Estimation of Gaussian Mixture Models
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.
Simulation of various radar angle measurement techniques including Maximum Likelihood Estimation and Monopulse Method, with algorithm implementation considerations