Add to ORKGSince the 1960s, many algorithms have been designed to deal with interval uncertainty. In the last decade, there has been a lot of progress in extending these algorithms to the case when we have a combination of interval and probabilistic uncertainty. We provide an overview of related algorithms, results, and remaining open problems
Traditional statistical data processing techniques (such as Least Squares) assume that we know the p...
In many practical situations, we are interested in statistics characterizing a population of objects...
Uncertainty is ubiquitous. Depending on what information we have, we get different types of uncertai...
In many practical problems, we need to process measurement results. For example, we need such data p...
In many practical problems, we need to process measurement results. For example, we need such data p...
In many practical situations, there is a need to combine interval and probabilistic uncertainty. The...
Different types of uncertainty are widely spread in all areas of human activity. Probabilistic uncer...
In many engineering problems, to estimate the desired quantity, we process measurement results and e...
Different types of uncertainty are widely spread in all areas of human activity. Probabilistic uncer...
Abstract — In many practical situations, there is a need to combine interval and probabilistic uncer...
Uncertainty is very important in risk analysis. A natural way to describe this uncertainty is to des...
In many engineering applications, we have to combine probabilistic, interval, and fuzzy uncertainty....
In many engineering applications, we have to combine probabilistic and interval uncertainty. For exa...
Abstract — The purpose of this paper is to present a new characterization of the set of all interval...
On various examples ranging from geosciences to environmental sciences, this book explains how to ge...
Traditional statistical data processing techniques (such as Least Squares) assume that we know the p...
In many practical situations, we are interested in statistics characterizing a population of objects...
Uncertainty is ubiquitous. Depending on what information we have, we get different types of uncertai...
In many practical problems, we need to process measurement results. For example, we need such data p...
In many practical problems, we need to process measurement results. For example, we need such data p...
In many practical situations, there is a need to combine interval and probabilistic uncertainty. The...
Different types of uncertainty are widely spread in all areas of human activity. Probabilistic uncer...
In many engineering problems, to estimate the desired quantity, we process measurement results and e...
Different types of uncertainty are widely spread in all areas of human activity. Probabilistic uncer...
Abstract — In many practical situations, there is a need to combine interval and probabilistic uncer...
Uncertainty is very important in risk analysis. A natural way to describe this uncertainty is to des...
In many engineering applications, we have to combine probabilistic, interval, and fuzzy uncertainty....
In many engineering applications, we have to combine probabilistic and interval uncertainty. For exa...
Abstract — The purpose of this paper is to present a new characterization of the set of all interval...
On various examples ranging from geosciences to environmental sciences, this book explains how to ge...
Traditional statistical data processing techniques (such as Least Squares) assume that we know the p...
In many practical situations, we are interested in statistics characterizing a population of objects...
Uncertainty is ubiquitous. Depending on what information we have, we get different types of uncertai...