In solving application problems, the data sets used to train a neural network may not be hundred percent precise but within certain ranges. Representing data sets with intervals, ...
Bayesian networks (BNs) have been widely used as a model for knowledge representation and probabilistic inferences. However, the single probability representation of conditional d...
Abstract. There exist several simple representations of uncertainty that are easier to handle than more general ones. Among them are random sets, possibility distributions, probabi...
We apply random set theory to an analysis of future climate change. Bounds on cumulative probability are used to quantify uncertainties in natural and socio-economic factors that ...
This paper is concerned with nonuniform sampling systems, where the sampling interval is time-varying within a certain known bound. The system is transformed into a time-varying d...