Comprehensive Probability Distribution Fitting Toolkit with Maximum Likelihood Estimation Algorithms

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.

Multiple Gaussian Mixture Model Estimation with Implementation Details

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.