Non-Destructive Sample Generation From Conditional Belief Functions

This work addresses a specific challenge in uncertainty modeling for researchers in belief function theory, but it is incremental as it builds on existing Bayesian network structures.

The paper tackles the problem of generating samples from conditional belief functions by assuming their factorization along a Bayesian network structure, resulting in a method applicable to a restricted but non-trivial subset of such functions.