What migration sets-out to do Deriving parameters for migration: 1D versus 3D assumptions Inversion Estimating image uncertainty Resolution scale length Generic model building loop for ray-based tomography Parametric versus non-parametric autopicking Wide azimuth and multi-azimuth data Anisotropic Model Building Anisotropic pre-stack depth migration in the absence of well control Resolving near...

Time series forecasting (i.e. the prediction of unknown future time series values using known data) has found several applications in a number of fields, like, economics and electricity forecasting [15]. Most of the used forecasting models deliver a so-called point forecast [4], a value that according to the models’ criteria is most likely to occur. Nonetheless, such forecasts lack information ...

To evaluate the conditional probability of an adverse outcome from a set of covariates, decision makers are often given a limited number of observations and, at times, are required to extrapolate outside the data range. To tackle the extrapolation problem they need to select plausible model(s) and account for various uncertainties in their predictions. In this paper I propose a framework that p...

Parameter perturbations in dynamical models of biochemical networks affect the qualitative dynamical behaviour observed in the model. Since this qualitative behaviour is in many cases the key model output used to explain biological function, the robustness analysis of the model’s behaviour with respect to parametric uncertainty is a crucial step in systems biology research. In this paper, we de...

Uncertainty evaluation for spatial prediction of curves remains an open issue in the functional data literature. We consider three different approaches that rely on semi-parametric bootstrapping, principal component analysis and classical inference for additive models respectively.

It is proposed that a numerical environmental model cannot be justified for predictive tasks without an implicit uncertainty analysis which uses reliable and transparent methods. Various methods of uncertainty-based model calibration are reviewed and demonstrated. Monte Carlo simulation of data, Generalised Likelihood Uncertainty Estimation (GLUE), the Metropolis algorithm and a set-based appro...

The uncertainty of rainfall estimated fiom averages of discrete samples collected by a satellite is assessed using a multi-year radar data set covering a large portion of the United States. The sampling-related uncertainty of rainfall estimates is evaluated for all combinations of 100 km, 200 km, and 500 km space domains, 1 day, 5 day, and 30 day rainfall accumulations, and regular sampling tim...

This paper presents a three-stage optimization algorithm for solving two-stage robust decision making problems under uncertainty with min-max regret objective. The structure of the first stage problem is a general mixed integer (binary) linear programming model with a specific model of uncertainty that can occur in any of the parameters, and the second stage problem is a linear programming mode...

In this paper we consider the problem of generating motion plans for a nonlinear dynamical system that are guaranteed to succeed despite uncertainty in the environment, parametric model uncertainty, disturbances, and/or errors in state estimation. Furthermore, we consider the case where these plans must be generated online, because constraints such as obstacles in the environment may not be kno...

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy NONLINEAR CONTROL FOR SYSTEMS CONTAINING INPUT UNCERTAINTY VIA A LYAPUNOV-BASED APPROACH By William MacKunis May 2009 Chair: Dr. Warren E. Dixon Major: Aerospace Engineering Controllers are often designed based on the assumption that a c...