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In this paper, we present a Bayesian method to model dispersal using spatial configuration and climatic data (distances between emitters and receptors; main wind direction) while accounting for uncertainty, with an application to the prediction of adventitious presence rate of genetically modified maize (GM) in a non-GM field.
One final consideration is the small number of studies which robustly control for uncertainty, with only the four most recently completed incorporating statistically robust techniques.
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After compensating for uncertainties with time-varying delays by combining appropriate Lyapunov-Krasovskii function with Young's inequality, an adaptive iterative learning control scheme is designed through neural approximation technique and Nussbaum function method.
To achieve structures that are risk-consistent, structural reliability methods must be used that account for uncertainties with respect to the relevant parameters affecting the estimation of wind effects.
For the multivariable analysis, we compared the commonly used stepwise variable selection approach with Bayesian model averaging (BMA) which provides a mechanism for accounting for model uncertainty with the aim of improving prediction accuracy [ 18, 19].
This procedure can therefore account for uncertainty associated with reconstructed phylogeny.
We built a Bayesian Markov model to account for uncertainty associated with the clinical input and cost data.
We adopted a simple Monte Carlo analysis to account for uncertainty associated with the model parameters, as well as natural variability.
As a consequence of these data limitations, current approaches are largely limited to applying default uncertainty factors to account for uncertainty associated with within-species variability (Stedeford et al. 2007).
This is one reason for the uncertainty with which the party projected itself.
Further, it is assumed that repressors (defined as intolerance for arousal but tolerance for uncertainty) would react with avoidance behavior when confronted with threatening stimuli.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com