Computational Function for Belief Function (bel) in DS Evidence Reasoning

In DS evidence reasoning, the belief function (bel) constitutes a critical component of computational processes. It enables the determination of hypothesis credibility under given evidence by implementing algorithms that combine sensor data, prior knowledge, and other available information. The computation typically involves mass function aggregation through Dempster's rule of combination, where the implementation may include normalization procedures and conflict handling mechanisms. Key functions often involve calculating belief measures for subsets of the frame of discernment using summation operations over supporting evidence. The technical implementation requires different approaches depending on specific scenarios, such as handling uncertain data through probabilistic assignments or managing evidence conflicts using specialized combination rules. Below we introduce several commonly used methodologies, including code structures for basic belief assignment processing and recursive combination algorithms, to facilitate better understanding of bel value computation. For comprehensive technical specifications including function signatures, input/output parameters, and algorithmic workflows, please refer to the detailed help documentation within this file.