Explore MATLAB source code curated for "概率分布" with clean implementations, documentation, and examples.
Computes joint probability density functions for multiple random variables with visualization capabilities, supporting distributions including Normal, Poisson, Gamma, and other common statistical functions with correlation analysis features.
This MATLAB program performs graphical analysis of three key probability distributions (t, F, and chi-square) by plotting curves under different parameter settings. The implementation allows visual comparison of how various parameters affect distribution shapes, utilizing MATLAB's statistical functions for accurate probability density function calculations.
Generating Random Variables Across up to 50 Probability Distributions
Implementation and Simulation of Poisson Process Algorithm in Stochastic Processes
Techniques for Generating Different Types of Random Numbers Using MATLAB with Code Examples
Particle Filter Implementation for Three-Dimensional Object Tracking with Code-Related Algorithm Explanations
MATLAB Examples for Generating Different Types of Random Numbers with Code Implementation Details
MATLAB implementation of Gaussian Mixture Model using EM algorithm capable of estimating three key parameters: mixing coefficients, mean vectors, and covariance matrices