IMPRECISE PROBABILITY: THEORIES AND APPLICATIONS

Prague, Czech Republic

16-19 July 2007

Imprecise probability is a generic term for the many mathematical or statistical models which allow us to measure chance or uncertainty without using sharp numerical probabilities. These models include belief functions, Choquet capacities, comparative probability orderings, convex sets of probability measures, fuzzy measures, interval-valued probabilities, possibility measures, plausibility measures, upper and lower expectations or previsions, and sets of desirable gambles. Imprecise probability models are needed in both inference and decision problems where the relevant information is scarce, vague or conflicting, and where preferences may therefore also be incomplete.

The actual symposium will take three days (17-19 July, 2007). It is preceded by a day devoted to tutorials (16 July 2007).

The symposium is open to contributions on all aspects of imprecise probability. But we do emphasize a number of themes that will get special attention: (i) algorithms and real applications, (ii) links between existing models, and (iii) theoretical results that facilitate using imprecise probability models in practice.

Topics of interest include, but are not limited to:

- models of coherent imprecise assessments
- convex sets of probability measures (credal sets)
- interval-valued probabilities
- upper and lower expectations or previsions
- non-additive set functions, and in particular Choquet capacities (and Choquet integration), fuzzy measures, possibility measures, belief and plausibility measures
- random sets
- rough sets
- comparative probability orderings
- qualitative reasoning about uncertainty
- imprecision in utilities and expected utilities
- limit laws for imprecise probabilities
- physical models of imprecise probability
- philosophical foundations for imprecise probabilities
- psychological models for imprecision and indeterminacy in probability assessments
- elicitation techniques for imprecise probabilities
- robust statistics
- probabilistic bounding analysis
- data mining with imprecise probabilities
- dealing with missing data
- estimation and learning of imprecise probability models
- decision making with imprecise probabilities
- ambiguity aversion and economic models of imprecise probability
- uncertainty in financial markets
- algorithms for manipulating imprecise probabilities
- Dempster-Shafer theory
- information algebras and probabilistic argumentation systems
- probabilistic logic, propositional and first-order
- credal networks and other graphical models
- credal classification
- applications in statistics, economics, finance, management, engineering, computer science and artificial intelligence, psychology, philosophy and related fields.

Send any remarks to the following address: smc@decsai.ugr.es