Name
Abstract | ||
|---|---|---|
A framework for quantifying lower and upper bipolar belief is introduced, which incorporates aspects of stochastic and of semantic uncertainty as well as an indeterministic truth-model allowing for inherent linguistic vagueness at the propositional level. This is then extended to include lower and upper measures of conditional belief given information in the form of lower and upper truth-valuations. The properties of these measures are explored and their relationship with conditional belief in other uncertainty theories is highlighted. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-39091-3_31 | ECSQARU |
Keywords | Field | DocType |
inherent linguistic vagueness,semantic uncertainty,indeterministic truth-model,upper bipolar belief,upper measure,uncertainty theory,upper truth-valuations,conditional belief,bipolar framework,propositional level | Dutch book,Vagueness,Uncertainty quantification,Computer science,Artificial intelligence,Machine learning | Conference |
Citations | PageRank | References |
3 | 0.43 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jonathan Lawry | 1 | 172 | 19.06 |
| Trevor Martin | 2 | 5 | 1.16 |