Sentence examples for model for uncertainty from inspiring English sources
Furthermore, a novel approach of building a statistical emulator of a simulation model for uncertainty analysis is presented.
RST is an extension of set theory, consisting of a mathematical model for uncertainty and imprecision handling, knowledge representation, and rough classification.
This approach is based on a process model for uncertainty analysis, the size range development methodology of Pahl and Beitz, and robust design principles.
Apart from wider degree of uncertainty domain coverage and economic viability, the use of modern experimental design method such as UD can offer more reliable proxy model for uncertainty quantification.
More specifically, Lambrecht et al. (2007) develop an empirical model for uncertainty under three-part tariffs (consisting of a fixed fee, a usage allowance, and pay-per-use price for the usage that exceeds the allowance) by incorporating future usage shocks occurring prior to the consumption decision in the tariff choice stage.
An explicit model for uncertainty in transcript profiling data could be used to formulate and then solve robust sparse linear regression problems and hence produce models of genetic networks that are more resilient to variation in training data than those generated using the Huber loss function considered here.
To satisfy the requirements of accurate position tracking and unrumpled retraction/deployment of the boom, it is essential to maintain energy equilibrium in the retracting/deploying process and simultaneously compensate the model for uncertainties when controlling the deployable boom.
The uncertainties related to fatigue crack initiation and to crack propagation are accounted for using a single model for uncertainties.
This paper compares four measures that have been advocated as models for uncertainty in expert systems.
DFM is a spatial state estimation and system identification methodology that uses three sources of information: measured data, physical laws, and statistical models for uncertainty in spatial heterogeneities.
This history matching reveals a clearer trade-off relationship between the matching qualities than conventional global-objective history matching, thereby being advantageous to yielding converged and diversified geological models for uncertainty quantification.
