International DST MATLAB Toolbox for Dempster-Shafer Theory Implementation

This is an international MATLAB toolbox for Dempster-Shafer Theory (DST), which comprehensively implements the mathematical framework for handling uncertainty and evidence combination. The toolbox includes core algorithms such as Dempster's rule of combination for merging evidence from multiple sources, belief and plausibility function calculations, and mass assignment operations. Key features include evidence simulation modules that generate synthetic data for testing, analytical tools for uncertainty quantification, and visualization components that plot belief intervals and evidence convergence. The implementation supports basic probability assignment (BPA) manipulation, conflict measurement between evidence bodies, and decision-making under uncertainty. Additionally, the toolbox provides comprehensive documentation with code examples demonstrating how to initialize mass functions, combine evidence using the combination rule, and visualize results through MATLAB's plotting functions. Tutorials guide users through practical applications like sensor fusion and pattern recognition, making it accessible for beginners while maintaining robustness for advanced research. Overall, this serves as an essential authoritative toolbox for learning DST fundamentals and applying them to real-world problem solving.