Implementation of Generalized Gaussian Distribution (GGD) Model

This MATLAB source code implements the Generalized Gaussian Distribution (GGD) model, including comprehensive testing procedures. The implementation includes maximum likelihood estimation (MLE) algorithms for fitting GGD parameters to datasets, providing more accurate distribution fitting compared to standard Gaussian models. The model's core functionality involves shape parameter (β) and scale parameter (σ) optimization through iterative numerical methods, enabling precise data characterization. The GGD model serves as a robust foundation for subsequent data analysis tasks, offering improved reliability in statistical modeling. Additionally, the model finds significant applications in image processing domains, particularly in image compression techniques where it efficiently models wavelet coefficient distributions. The implementation includes specialized functions for probability density calculation (ggd_pdf.m), parameter estimation (ggd_fit.m), and model validation (ggd_test.m), demonstrating broad applicability and high practical value across various engineering and scientific fields.