Faithful Approximations of Belief Functions
This work addresses the computational intractability in belief function approximations, which is important for researchers in uncertainty reasoning and decision-making, but it appears incremental as it builds on earlier methods.
The paper tackles the problem of approximating belief functions by proposing a conceptual foundation based on consistency and closeness, and studies an optimal but intractable approximation. It introduces heuristic methods that are experimentally evaluated for accuracy and speed, showing improvements over earlier approximations, though specific numerical results are not provided in the abstract.
A conceptual foundation for approximation of belief functions is proposed and investigated. It is based on the requirements of consistency and closeness. An optimal approximation is studied. Unfortunately, the computation of the optimal approximation turns out to be intractable. Hence, various heuristic methods are proposed and experimantally evaluated both in terms of their accuracy and in terms of the speed of computation. These methods are compared to the earlier proposed approximations of belief functions.