Dempster-Shafer Theory (DST) : Core Insights, Applications and Challenges!!
Abstract: Dempster-Shafer theory (DST) is a framework for reasoning with uncertainty and making decisions when there isn't enough information. It's a generalization of probability theory that allows for incomplete knowledge by assigning a probability mass to each subset of a domain, rather than each element. Here are some key features of DST: Combining evidence DST uses Dempster's rule of combination to combine evidence from different sources. This rule creates a shared belief between sources and distributes non-shared belief through a normalization factor. Representing uncertainty DST uses belief intervals to quantify uncertainty. Each basic probability value has a corresponding belief interval. Handling unassigned belief Unlike classical probability theory, DST allows some belief to be unassigned to any of the candidate conclusions. This represents a state of ignorance when there isn't enough knowledge. DST is used in many areas of computer