The Dempster-Shafer (DS) theory of belief functions, also referred to as evidence theory, was first introduced by Dempster 1, 2 in the context of statistical inference. It was later developed by Shafer 3 into a general framework for uncertainty modeling. In the recent years, it has been transformed into an important computational tool for evidential reasoning in artificial intelligence.
Computation of Conditionals
The conditional operation plays an important role in DS theoretic (DST) framework. This library is developed to carry out computations on Dempster's rule of conditioning and Fagin-Halpern conditioning, the two most widely utilized DST conditional strategies in reasoning under uncertainty.