Fuzzy probability theory is an extension to probability theory to dealing with a mix of
probabilistic uncertainty and non-probabilistic imprecision. It provides a theoretical basis to …

In some practical situations–eg, when treating a new illness–we do not have enough data to
make valid statistical conclusions. In such situations, it is necessary to use expert knowledge …

Security testing is a process of determining risks present in the system states and protects
them from vulnerabilities. But security testing does not provide due importance to threat …

In geophysics, we usually have several Earth models based on different types of data:
seismic, gravity, etc. Each of these models captures some aspects of the Earth structure. To …

Motivation. Data uncertainty affects the results of data processing. So, it is necessary to find
out how the data uncertainty propagates into the uncertainty of the results of data …

Scientific team members often publish datasets, posters, workflows, and publications at
different locations over the Web. If they were to share their information structured and linked …

To properly process data, we need to know the accuracy of different data points, ie, accuracy
of different measurement results and expert estimates. Often, this accuracy is not given. For …

In this paper, we describe how to estimate relative spatial resolution of different maps or
images of the same area under uncertainty. We consider probabilistic and fuzzy approaches …

In many application areas, we need to fuse continuous and discrete models of the same
phenomena. For example, in geophysics, we have two main models for describing how the …

2. Problem• Often, the differences∆ rx=∆ x−∆ sx corr. to nearby times are strongly correlated.•
For example, meteorological sensors may have daytime or nighttime biases, or winter and …