Systematic uncertainties in high energy physics and astrophysics are often significant contributions to the overall uncertainty in a measurement, in many cases being comparable to the statistical uncertainties. However, consistent definition and practice is elusive, as there are few formal definitions and there exists significant ambiguity in what is defined as a systematic and statistical unce...

In this article, we analyze the impact of uncertainty on the hiring process. We show the connection between models of statistical discrimination where uncertainty can work against groups that have less reliable indicators of future productivity and models of option value where uncertainty about future productivity can be beneficial for these groups. These models generate hypotheses about the re...

Tractography allows us to explore white matter connectivity in diffusion MR images of the brain. However, noise, artifacts and limited resolution introduce uncertainty into the results. We propose a statistical model that allows us to quantify and visualize the uncertainty of a neuronal pathway between any two fixed anatomical regions. Given a sample set of tract curves obtained via tractograph...

Stability and performance of a system can be inferred from the evolution of statistical characteristic (i.e. mean, variance...) of system states. The polynomial chaos of Wiener provides a computationally effective framework for uncertainty quantification of stochastic dynamics in terms of statistical characteristic. In this work, polynomial chaos is used for uncertainty quantification of fracti...

We study the empirical likelihood approach to construct confidence intervals for the optimal value and the optimality gap of a given solution, henceforth quantify the statistical uncertainty of sample average approximation, for optimization problems with expected value objectives and constraints where the underlying probability distributions are observed via limited data. This approach relies o...

Visualization is a powerful way to facilitate data analysis, but it is crucial that visualization systems explicitly convey the presence, nature, and degree of uncertainty to users. Otherwise, there is a danger that data will be falsely interpreted, potentially leading to inaccurate conclusions. A common method for denoting uncertainty is to use error bars or similar techniques designed to conv...

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 ...