Graphical models—especially probabilistic networks like Bayes networks and Markov networks—are very popular to make reasoning in highdimensional domains feasible. Since constructing them manually can be tedious and time consuming, a large part of recent research has been devoted to learning them from data. However, if the dataset to learn from contains imprecise information in the form of sets ...

Many methods have been proposed to mine fuzzy association rules from databases with crisp values in order to help decision-makers make good decisions and tackle new types of problems. However, most real-world problems present a certain degree of imprecision. Various studies have been proposed to mine fuzzy association rules from imprecise data but they assume that the membership functions are k...

Financial surveillance before the current crisis erupted suggested that problems were forming but the indications were too imprecise to permit a policy response. Work is currently being undertaken to improve the measurement, monitoring and management of systemic risk. That requires it to be defined, which is unproblematic, and operationalized, which is not. While promising methods to measure ri...

We consider universal relations as flat tables having numeric data types which can be simply viewed as spreadsheets. The underlying stochastic model is the finite dimensional metric (Euclidean) space R backed up by the Borel set B as an adequate σ-algebra, and a related uncertainty (see Fuzzy Logic) or probability measure P, i.e. (R,B,P)as the corresponding possibility or probability space. You...

Several recent works have focused on OLAP over imprecise data, where each fact can be a region, instead of a point, in a multidimensional space. They have provided a multiple-world semantics for such data, and developed efficient solutions to answer OLAP aggregation queries over the imprecise facts. These solutions however assume that the imprecise facts can be interpreted independently of one ...