Deriving And Combining Continuous Possibility Functions in the Framework of Evidential Reasoning
This work addresses a methodological bottleneck in evidential reasoning for researchers in uncertainty modeling, though it appears incremental as it builds on existing methods.
The paper tackled the problem of incorporating continuous statistical information into the Dempster-Shafer framework by deriving continuous possibility and mass functions from probability-density functions and proposing a simpler, more efficiently computed combination rule than Dempster's rule.
To develop an approach to utilizing continuous statistical information within the Dempster- Shafer framework, we combine methods proposed by Strat and by Shafero We first derive continuous possibility and mass functions from probability-density functions. Then we propose a rule for combining such evidence that is simpler and more efficiently computed than Dempster's rule. We discuss the relationship between Dempster's rule and our proposed rule for combining evidence over continuous frames.