Conclusions Markov-reward models are often used to analyze the reliability & performability of computer systems. One difficult problem therein is the quantification of the model parameters. If they are available, eg, from measurement data collected by manufacturers, they are, a) generally regarded as confidential, and b) difficult to access. This paper addresses two ways of dealing with uncerta...

Many articles have been published on the uncertainty analysis of high temperature gas kinetic systems that are based on detailed reaction mechanisms. In all these articles a temperature independent relative uncertainty of the rate coefficient is assumed, although the chemical kinetics databases suggest temperature dependent uncertainty factors for most of the reactions. The temperature dependen...

Uncertainty importance measures typically reflect the degree to which uncertainty about risk and reliability parameters at the component level influences uncertainty about parameters at the system level. The definition of these measures is typically founded on a Bayesian perspective where subjective probabilities are used to express epistemic uncertainty; hence, they do not reflect the effect o...

Risk assessment is an important and popular aid in the decision making process. The aim of risk assessment is to estimate the severity and likelihood of harm to human health from exposure to a substance or activity that under plausible circumstances can cause to human health. In risk assessment, it is most important to know the nature of all available information, data or model parameters. More...

The Johnson-Ettinger Model is widely used for assessing the impacts of contaminated vapors on residential air quality. Typical use of this model relies on a suite of estimated data, with few site-specific measurements. Software was developed to provide the public with automated uncertainty analysis applied to the model. (See http://www.epa.gov/athens/onsite.) An uncertainty analysis was perform...

Uncertainty in cloud microphysical parameterization—a leading order contribution to numerical weather prediction error—is estimated using a Markov chain Monte Carlo (MCMC) algorithm. An inversion is performed on 10 microphysical parameters using radar reflectivity observations with vertically covarying error as the likelihood constraint. An idealized 1D atmospheric column model with prescribed ...

Comparison of experimental data with modeling predictions is essential for making quantitative measurements of species properties, such as diffusion coefficients and species concentrations using a T-sensor. To make valid comparisons between experimental data and model predictions, it is necessary to account for uncertainty in model predictions due to uncertain values of model parameters. We pre...

This paper deals with the ego-motion estimation (motion of the camera) from two views. When we want to estimate the ego-motion we have to nd correspondences and we need a calibrated camera. In this paper we solve the problem how to propagate known camera calibration errors into the uncertainty of the motion parameters. We present a linear estimate of the uncertainty of the motion parameters bas...