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To reduce the conservatism of the guaranteed cost control framework, uncertainty scalings such as those used in the D-K iteration heuristic for µ-synthesis are introduced.
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Within the Bayesian framework, uncertainties are obtained from the posterior densities.
Within the frequentist framework, uncertainties are obtained i) from the Gaussian density stemming from the asymptotic normality theorem of the maximum likelihood and ii) with a bootstrap procedure.
With this newly proposed PESQ coding framework, uncertainties in the assignments of codes are expressed using probabilities that are assigned to each particular coding value.
These weights are then employed to (a) rank the models according to their relative skill to interpret the observations and (b) obtain model averaged results which allow accommodating within a unified theoretical framework uncertainties arising from differences amongst model structures.
Finally, we investigate the notion of no dominance, proposing its robust counterpart in the model uncertainty framework and studying its consequences on the concept of bubble.
We demonstrated a generic info-gap framework for managing model uncertainty in public health decision making.
The BMA framework accounts for model uncertainty in variable selection by averaging over the posterior distributions from multiple models, weighted by their posterior model probabilities [ 13, 14].
After the proposed framework for uncertainty analysis is explained the uncertainties pertaining to the design and operation of residential DERs and micro-CHP systems are identified.
In the probabilistic framework, this uncertainty directly propagates through to the overall uncertainty in the outcome of the dose response assessment.
This paper clarifies these uncertainties by proposing and applying a comprehensive framework for uncertainty analysis.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com