a European Commission, Joint Research Centre, Institute for Environment and Sustainability, Ispra, 21027(VA), Italy – (jon.skoien, gregoire.dubois) @jrc.ec.europa,eu b Wageningen University, Land Dynamics Group, Wageningen, the Netherlands – (phuong.truong, gerard.heuvelink) @wur.nl c NCRG and Computer Science, Aston University, Birmingham, UK [email protected] d NASA Jet Propulsion Labora...

This paper presents an overview of various aspects of uncertainty quantification in prognostics and health management. Since prognostics deals with predicting the future behavior of engineering systems and it is almost practically impossible to precisely predict future events, it is necessary to account for the different sources of uncertainty that affect prognostics, and develop a systematic f...

Uncertainty propagation and quantification has gained considerable research attention during recent years. In this paper we consider uncertainty propagation and quantification in a continuous-time dynamical system governed by ordinary differential equations with uncertain/stochastic components. Specifically, we focus on the time evolution of probability density functions of the resulting stocha...

In this paper, a methodology for propagation of uncertainty in stochastic nonlinear dynamical systems is investigated. The process noise is approximated using KarhunenLoève (KL) expansion. Perron-Frobenius (PF) operator is used to predict the evolution of uncertainty. A multivariate Kolmogorov-Smirnov test is used to verify the proposed framework. The method is applied to predict uncertainty ev...

Uncertainty decoding and uncertainty propagation, or error propagation, techniques have emerged as a powerful tool to increase the accuracy of automatic speech recognition systems by employing an uncertain, or probabilistic, description of the speech features rather than the usual point estimate. In this paper we analyze the uncertainty generated in the complex Fourier domain when performing sp...

OBJECTIVES Bayesian interpretation of diagnostic test results usually involves point estimates of the pretest probability and the likelihood ratio corresponding to the test result; however, it may be more appropriate in clinical situations to consider instead a range of possible values to express uncertainty in the estimates of these parameters. We thus sought to demonstrate how uncertainty in ...

In order to model the distribution of input variables, a reasonable assumption is that input variables x are generated by some process u, and that process has a Gaussian error. Thus, the distribution of input variables can be presented as ppxq N pu,Σxq. The new data point for prediction will be annotated as x . In the general case, we predict on the entire set of points representing a single sn...

Macroscopically heterogeneous materials, characterised mostly by comparable heterogeneity lengthscale and structural sizes, can no longer be modelled by deterministic approach instead. It is convenient to introduce stochastic approach with uncertain material parameters quantified as random fields and/or random variables. The present contribution is devoted to propagation of these uncertainties ...

Probability-boxes, numerical possibility theory and belief functions have been suggested as useful tools to represent imprecise, vague or incomplete information. They are particularly appropriate in environmental risk assessment where information is typically tainted with imprecision or incompleteness. Based on these notions, we present and compare four different methods to propagate objective ...

A Gaussian mixture model approach is proposed for accurate uncertainty propagation through a general nonlinear system. The transition probability density function, is approximated by a finite sum of Gaussian density functions whose parameters (mean and covariance) are propagated using linear propagation theory. Two different approaches are introduced to update the weights of different component...