The statistical methods applied to the analysis of genomic data do not account for uncertainty in the sequence alignment. Indeed, the alignment is treated as an observation, and all of the subsequent inferences depend on the alignment being correct. This may not have been too problematic for many phylogenetic studies, in which the gene is carefully chosen for, among other things, ease of alignm...

This paper discusses the analysis of uncertainties in structural analysis of space crafts. Various sources for the discrepancies between prediction and measured response are identified. It is shown that only the effect of scatter of the structural parameters can be studied further by appropriate analysis tools. It is suggested to model uncertainties mathematically within the framework of stocha...

Accurate modelling of information and knowledge is central to the modern command and control (C2) process. Without models and a language for describing them, it is impossible to collaborate on C2. All information which enters a C2 system will be uncertain, and hence it is important to be able to model the uncertainty in a way that makes it possible for us to understand it. Some kinds of knowled...

Interim Reports on work of the International Institute for Applied Systems Analysis receive only limited review. Views or opinions expressed herein do not necessarily represent those of the Institute, its National Member Organizations, or other organizations supporting the work. Abstract This paper aims to give an overview of the possibilities for using automatic differentiation for uncertainty...

In this paper, we describe an approach to scheduling under uncertainty that achieves scalability through a coupling of deterministic and probabilistic reasoning. Our specific focus is a class of oversubscribed scheduling problems where the goal is to maximize the reward earned by a team of agents in a distributed execution environment. There is uncertainty in both the duration and outcomes of e...

Although a number of theories and principles have been developed to guide the creation of visualizations, it is not always apparent how to apply the knowledge in these principles. We describe the application of perceptual and cognitive theories for the analysis of uncertainty visualizations. General principles from Bertin, Tufte, and Ware are outlined and then applied to the analysis of eight d...

Gromov’s non-squeezing theorem is a relatively new results in symplectic topology that provides a non-trivial constraint on the propagation of uncertainty in a Hamiltonian system. While the application to nonlinear systems is difficult, one can apply the theorem to obtain bounds on the propagation of uncertainty in the linearized system, by exploiting the fact that the flow of the linearization...

Bayesian Model Calibration is used to revisit the problem of scaling factor calibration for semi-empirical correction of ab initio calculations. A particular attention is devoted to uncertainty evaluation for scaling factors, and to their effect on prediction of observables involving scaled properties. We argue that linear models used for calibration of scaling factors are generally not statist...

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....