Explore MATLAB source code curated for "方差" with clean implementations, documentation, and examples.
This MATLAB implementation solves parameter estimation for Gaussian Mixture Models using the Expectation-Maximization (EM) algorithm. The program modularly separates mean, covariance, and weight estimation into independent functions saved as .M files. The main execution point is through main.m, with sample data provided in spreadsheet format for immediate testing and customization.
For parameter estimation problems, the Cramér-Rao Bound (CRB) establishes a lower bound on the variance of any unbiased estimator. This means it's impossible to obtain an unbiased estimator with variance smaller than this bound, providing a benchmark for comparing the performance of unbiased estimators. This program implements the Cramér-Rao Lower Bound (CRLB) with parameterized design, allowing users to customize parameters according to their specific estimation scenarios and requirements.
Gaussian fitting program utilizing Gaussian functions to fit data, extracting mean and variance parameters of the fitted Gaussian distribution, with implementation details for MATLAB's curve fitting toolbox and custom optimization approaches.
MATLAB implementation for function normalization to achieve zero mean and unit variance, enabling standardized data preprocessing for comparative analysis.
Gabor features including mean and variance with concise interface functions, extremely practical for implementation!
Functions for generating Gaussian white noise with zero mean and specified variance, along with complex Gaussian white noise generation capabilities, including MATLAB implementation examples and algorithm explanations.
This MATLAB-based program implements the extraction of various parameter features essential for histogram analysis, including mean, variance, skewness, kurtosis, energy, and entropy. The code utilizes built-in statistical functions and custom calculations to accurately quantify distribution characteristics.
MATLAB-based implementation of the Expectation-Maximization algorithm for estimating means, variances, and weights in Gaussian Mixture Models, featuring iterative optimization and parameter estimation capabilities.
Generating narrowband random processes based on Rice representation of stochastic processes, including mastering their key characteristics such as mean (mathematical expectation), variance, correlation function, and power spectral density through implementation approaches involving random variable generation and spectral analysis techniques.
Computation of histogram characteristics including variance, kurtosis, and entropy values, with implementation insights.