A computer assisted modelling methodology is developed for the generation of linearized models with parametric uncertainties described by Linear Fractional Transformations (LFTs). The starting point of the uncertainty modelling is a class of generic nonlinear aircraft models with explicit parametric dependence used for simulation purposes. The proposed methodology integrates specialized softwar...

To formalise our discussion of model uncertainty we will rely on probabilistic modelling, and more specifically on Bayesian modelling. Bayesian probability theory offers us the machinery we need to develop our tools. Together with techniques for approximate inference in Bayesian models, in the next chapter we will present the main results of this work. But prior to that, let us review the main ...

Most models will attempt to extract losses (interception, surface storage, infiltration) from the input and build a runoff hydrograph using empirical curves or by routing the excess water. Even the simplest models generally involve many parameters to describe these processes. Some parameters are estimated from published values, and others are set based on past practice and calibration of the mo...

Distributed reasoning in M2M leverages the expressive power of ontology to enable semantic interoperability between heterogeneous systems of connected devices. Ontology, however, lacks the built-in, principled support to effectively handle the uncertainty inherent in M2M application domains. Thus, efficient reasoning can be achieved by integrating the inferential reasoning power of probabilisti...

Numerical induction models are considered in this report to be models that aggregate lower-level information into higher-level measures for decision making. Various forms of uncertainty may be present in such models including hybrid uncertainties within the information elements being aggregated. After a review of some existing approaches for representing higher-order uncertainty in information,...

Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often u...

Topic of this contribution is the integration of data uncertainty in complex analysis processes. By means of a flooding forecast as an example application, the enhancements which are necessary in data modelling and analysis functions are explained. The major objective of the research is to determine the uncertainty in the spatial extension of the possible flooded region. In particular, the impa...

Response surface methodology (RSM) relies on the design of experiments and empirical modelling techniques to find the optimum of a process when the underlying fundamental mechanism of the process is largely unknown. This paper proposes an iterative RSM framework, where Gaussian process (GP) regression models are applied for the approximation of the response surface. GP regression is flexible an...

The modelling of dynamic effects for flight dynamics analysis typically makes use of dynamic derivatives. A single value for the derivative is used and is assumed to be independent of the conditions for which it is calculated. This is however not the case. This work looks to quantify the uncertainty introduced as a result for an arbitrary manoeuvre within the flight envelope. The manoeuvres can...

This paper addresses the issue of modelling the uncertainty about the value of continuous soil Ž . attributes, at any particular unsampled location local uncertainty as well as jointly over several Ž . locations multiple-point or spatial uncertainty . Two approaches are presented: kriging-based and Ž . simulation-based techniques that can be implemented within a parametric e.g. multi-Gaussian o...