Probabilistic relational models (PRMs) are a rich representation language for structured statistical models. They combine a frame-based logical representation with probabilistic semantics based on directed graphical models (Bayesian networks). This chapter gives an introduction to probabilistic relational models, describing semantics for attribute uncertainty, structural uncertainty, and class ...

Uncertain statistics is a methodology for collecting and interpreting experts’ experimental data by using uncertainty theory. Based on empirical uncertainty distributions, this paper will present a statistical method of uncertain hypothesis testing to detect whether two uncertainty distributions are equal. © 2011 Elsevier Ltd. All rights reserved.

This paper deals with the analysis of the uncertainty of the fundamental matrix. The basic idea is to compute the fundamental matrix and its uncertainty at the same time. We give two diierent methods. The rst one is a statistical approach. As in all statistical methods the precision of the results depends on the number of analyzed samples. This means that we can always improve our results if we...

Due to time and financial restrictions in an educational laboratory, we are making compromises, using experimental setups in which limitations and uncertainties are important. In these cases we should pay particular attention to the role of different factors that affect our experiment, in order to achieve the best possible educational outcome and to avoid misconceptions. In this paper problems ...

uncertainty associated with mixing models is often substantial, but has not yet been fully incorporated in models. the objective of this study is to develop and apply a bayesian-mixing model that estimates probability distributions of source contributions to a mixture associated with multiple sources for assessing the uncertainty estimation in sediment fingerprinting in zidasht catchment, iran....

Traditional statistical and fuzzy approaches to describing uncertainty are continuous in the sense that we use a (potentially infinite) set of values from the interval [0; 1] to characterize possible degrees of uncertainty. In reality, experts describe their degree of belief by using one of the finitely many words from natural language; in this sense, the actual description of expert uncertaint...

Typical statistical analysis of epidemiologic data captures uncertainty due to random sampling variation, but ignores more systematic sources of variation such as selection bias, measurement error, and unobserved confounding. Such sources are often only mentioned via qualitative caveats, perhaps under the heading of 'study limitations.' Recently, however, there has been considerable interest an...

n many DoD test and evaluation programs, it is necessary to obtain statistical estimates for parameters in the system under study. For these estimates to provide meaningful system understanding, uncertainty bounds (e.g., statistical confidence intervals) must be attached to the estimates. Current methods for constructing uncertainty bounds are almost all based on theory that assumes a large amo...