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 ...

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 ...

Many software test generation techniques target on generating software test data. Only a few of them provide automatic way to verify if software behaves correctly using generated test data. We propose a testing technique, which uses UML modeling language extension OCL as imprecise test oracle. Imprecise OCL constraints can be viewed as expressions which define expected results within some range...

Abstract The classical credibility theory is a cornerstone of experience rating, especially in the field property and casualty insurance. An obstacle to putting into practice conversion available prior information precise choice crucial hyperparameters. In most real-world applications, necessary justify lacking, so we propose an imprecise estimator that honestly acknowledges imprecision hyperpa...

The objective of this work is to quantify the uncertainty in probability failure estimates resulting from incomplete knowledge distributions for input random variables. We propose a framework that couples widely used Subset simulation (SuS) with Bayesian/information theoretic multi-model inference. process starts data infer model inputs. Often such sets are small. Multi-model inference assess a...