The Experts below are selected from a list of 124893 Experts worldwide ranked by ideXlab platform
Mikael Mortensen - One of the best experts on this subject based on the ideXlab platform.
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consistent modeling of scalar mixing for presumed multiple Parameter Probability density functions
Physics of Fluids, 2005Co-Authors: Mikael MortensenAbstract:In this Brief Communication we describe a consistent method for calculating the conditional scalar dissipation (or diffusion) rate for inhomogeneous turbulent flows. The model follows from the transport equation for the conserved scalar Probability density function (PDF) using a gradient diffusion closure for the conditional mean velocity and a presumed PDF depending on any number of mixture fraction moments. With the presumed β PDF, the model is an inhomogeneous modification to the homogeneous model of Girimaji ["On the modeling of scalar diffusion in isotropic turbulence," Phys. Fluids A 4, 2529 (1992)]. An important feature of the model is that it makes the classical approach to the conditional moment closure completely conservative for inhomogeneous flows.
Michael D Todd - One of the best experts on this subject based on the ideXlab platform.
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an efficient metamodeling approach for uncertainty quantification of complex systems with arbitrary Parameter Probability distributions
International Journal for Numerical Methods in Engineering, 2017Co-Authors: Michael D ToddAbstract:Summary This paper proposes an efficient metamodeling approach for uncertainty quantification of complex system based on Gaussian process model (GPM). The proposed GPM-based method is able to efficiently and accurately calculate the mean and variance of model outputs with uncertain Parameters specified by arbitrary Probability distributions. Because of the use of GPM, the closed form expressions of mean and variance can be derived by decomposing high-dimensional integrals into one-dimensional integrals. This paper details on how to efficiently compute the one-dimensional integrals. When the Parameters are either uniformly or normally distributed, the one-dimensional integrals can be analytically evaluated, while when Parameters do not follow normal or uniform distributions, this paper adopts the effective Gaussian quadrature technique for the fast computation of the one-dimensional integrals. As a result, the developed GPM method is able to calculate mean and variance of model outputs in an efficient manner independent of Parameter distributions. The proposed GPM method is applied to a collection of examples. And its accuracy and efficiency is compared with Monte Carlo simulation, which is used as benchmark solution. Results show that the proposed GPM method is feasible and reliable for efficient uncertainty quantification of complex systems in terms of the computational accuracy and efficiency. Copyright © 2016 John Wiley & Sons, Ltd.
Brett A Hauber - One of the best experts on this subject based on the ideXlab platform.
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eliciting benefit risk preferences and Probability weighted utility using choice format conjoint analysis
Medical Decision Making, 2011Co-Authors: George Van Houtven, Reed F Johnson, Vikram Kilambi, Brett A HauberAbstract:This study applies conjoint analysis to estimate health-related benefit-risk tradeoffs in a non-expected-utility framework. We demonstrate how this method can be used to test for and estimate nonlinear weighting of adverse-event probabilities and we explore the implications of nonlinear weighting on maximum acceptable risk (MAR) measures of risk tolerance. We obtained preference data from 570 Crohn’s disease patients using a web-enabled conjoint survey. Respondents were presented with choice tasks involving treatment options that involve different efficacy benefits and different mortality risks for 3 possible side effects. Using conditional logit maximum likelihood estimation, we estimate preference Parameters using 3 models that allow for nonlinear preference weighting of risks—a categorical model, a simple-weighting model, and a rank dependent utility (RDU) model. For the second 2 models we specify and jointly estimate 1- and 2-Parameter Probability weighting functions. Although the 2-Parameter function...
V Solana - One of the best experts on this subject based on the ideXlab platform.
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entropy based inference of simple physical models for regional flood analysis
Stochastic Environmental Research and Risk Assessment, 2001Co-Authors: A Solanaortega, V SolanaAbstract:Regional flood analysis is formulated as a physical-modelling problem consisting in the inference of meaningful physical models for a set of observable uncertain quantities representing floods, given the observed data separately associated with them. It is argued that physical modelling suitable for representing causality relationships should involve the use of models comprising functional dependences of the observable uncertain quantities with regard to other quantities which are unobservable. The regional physical-modelling problem becomes the selection, from any proposed space of candidate models, of a Probability distribution for the unobservable uncertain quantities together with a functional-dependence model connecting the observable to the unobservable uncertain quantities. Due to the need to coherently represent observational data and to express precisely the available evidences, the physical modelling problem is formalized in a plausible logic language, within the logical Probability framework. A logical inference procedure called the relative entropy method with fractile constraints (REF) is formulated within this framework and extended to solve the regional physical-modelling problem. Contrary to the current statistical methods, it allows the selection and validation of inferred models and can be applied whatever it is the number of observational data. The complete solution to the problem using the relative entropy procedure is presented. This method is applied to the regional modelling of annual maximum floods of a set of separate rivers in the Iberian Peninsula. For this application the space of candidate models includes several types of two-Parameter Probability distributions for the unobservable uncertain quantities and the class of linear homogeneous functional-dependence models connecting the observable to the unobservable quantities.
George Van Houtven - One of the best experts on this subject based on the ideXlab platform.
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eliciting benefit risk preferences and Probability weighted utility using choice format conjoint analysis
Medical Decision Making, 2011Co-Authors: George Van Houtven, Reed F Johnson, Vikram Kilambi, Brett A HauberAbstract:This study applies conjoint analysis to estimate health-related benefit-risk tradeoffs in a non-expected-utility framework. We demonstrate how this method can be used to test for and estimate nonlinear weighting of adverse-event probabilities and we explore the implications of nonlinear weighting on maximum acceptable risk (MAR) measures of risk tolerance. We obtained preference data from 570 Crohn’s disease patients using a web-enabled conjoint survey. Respondents were presented with choice tasks involving treatment options that involve different efficacy benefits and different mortality risks for 3 possible side effects. Using conditional logit maximum likelihood estimation, we estimate preference Parameters using 3 models that allow for nonlinear preference weighting of risks—a categorical model, a simple-weighting model, and a rank dependent utility (RDU) model. For the second 2 models we specify and jointly estimate 1- and 2-Parameter Probability weighting functions. Although the 2-Parameter function...