Add to ORKGIn many practical situations, there is a need to combine interval and probabilistic uncertainty. The need for such a combination leads to two types of problems: (1) how to process the given combined uncertainty, and (2) how to gauge the amount of uncertainty and -- a related question -- how to best decrease this uncertainty. In our research, we concentrate on these two types of problems. In this paper, we present two examples that illustrate how the corresponding problems can be solved
In many areas of science and engineering, it is desirable to estimate statistical characteristics (m...
To predict values of future quantities, we apply algorithms to the current and past measurement resu...
On various examples ranging from geosciences to environmental sciences, this book explains how to ge...
Abstract — In many practical situations, there is a need to combine interval and probabilistic uncer...
In many practical problems, we need to process measurement results. For example, we need such data p...
Since the 1960s, many algorithms have been designed to deal with interval uncertainty. In the last d...
In many engineering applications, we have to combine probabilistic and interval uncertainty. For exa...
In many engineering applications, we have to combine probabilistic and interval errors. For example,...
In many practical situations, the quantity of interest is difficult to measure directly. In such sit...
In high performance computing, when we process a large amount of data, we do not have much informati...
Abstract. In many engineering applications, we have to combine probabilistic and interval errors. Fo...
The paper is a continuation of our previous work towards the use of probability information in inter...
Uncertainty is ubiquitous. Depending on what information we have, we get different types of uncertai...
Abstract. In many engineering applications, we have to combine probabilistic and interval errors. Fo...
In many practical situations, for some components of the uncertainty (e.g., of the measurement error...
In many areas of science and engineering, it is desirable to estimate statistical characteristics (m...
To predict values of future quantities, we apply algorithms to the current and past measurement resu...
On various examples ranging from geosciences to environmental sciences, this book explains how to ge...
Abstract — In many practical situations, there is a need to combine interval and probabilistic uncer...
In many practical problems, we need to process measurement results. For example, we need such data p...
Since the 1960s, many algorithms have been designed to deal with interval uncertainty. In the last d...
In many engineering applications, we have to combine probabilistic and interval uncertainty. For exa...
In many engineering applications, we have to combine probabilistic and interval errors. For example,...
In many practical situations, the quantity of interest is difficult to measure directly. In such sit...
In high performance computing, when we process a large amount of data, we do not have much informati...
Abstract. In many engineering applications, we have to combine probabilistic and interval errors. Fo...
The paper is a continuation of our previous work towards the use of probability information in inter...
Uncertainty is ubiquitous. Depending on what information we have, we get different types of uncertai...
Abstract. In many engineering applications, we have to combine probabilistic and interval errors. Fo...
In many practical situations, for some components of the uncertainty (e.g., of the measurement error...
In many areas of science and engineering, it is desirable to estimate statistical characteristics (m...
To predict values of future quantities, we apply algorithms to the current and past measurement resu...
On various examples ranging from geosciences to environmental sciences, this book explains how to ge...