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Uncertainty Quantification of Central Canal Stenosis Deep Learning Classifier From Lumbar Sagittal T2-Weighted MRI. [PDF]

JOR Spine
Brenzikofer A, Monzon M, Galbusera F, Manjaly ZM, Cina A, Jutzeler CR.   +5 more
europepmc   +1 more source

Model Uncertainty, State Uncertainty, and State-Space Models [PDF]

SSRN Electronic Journal, 2012
State-space models have been increasingly used to study macroeconomic and financial problems. A state-space representation consists of two equations, a measurement equation which links the observed variables to unobserved state variables and a transition equation describing the dynamics of the state variables.
Young, ER, Luo, Y, Nie, J
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Agents’ model of uncertainty

Knowledge and Information Systems, 2008
Multi-agent systems play an increasing role in sensor networks, software engineering, web design, e-commerce, robotics, and many others areas. Uncertainty is a fundamental property of these areas. Agent-based systems use probabilistic and other uncertainty models developed earlier without explicit consideration of agents. This paper explores the impact
Germano Resconi, Boris Kovalerchuk
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Measurement, Models, and Uncertainty

IEEE Transactions on Instrumentation and Measurement, 2012
Against the tradition, which has considered measurement able to produce pure data on physical systems, the unavoidable role played by the modeling activity in measurement is increasingly acknowledged, particularly with respect to the evaluation of measurement uncertainty.
Giordani, Alessandro, Mari, Luca Paolo
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MODEL UNCERTAINTY AND SCENARIO AGGREGATION

Mathematical Finance, 2014
This paper provides a coherent method for scenario aggregation addressing model uncertainty. It is based on divergence minimization from a reference probability measure subject to scenario constraints. An example from regulatory practice motivates the definition of five fundamental criteria that serve as a basis for our method.
Cambou, Mathieu, Filipovic, Damir
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Model Uncertainty

Mental models help people navigate complex environments. This paper studies how people deal with model uncertainty. In an experiment, participants estimate a company's value, facing uncertainty about which one of two models correctly deter- mines its true value. Using a between-subjects design, we vary the degree of model complexity.
Musolff, R., Zimmermann, F.
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Uncertainty in Climate Models

Science, 2002
Thomas M. Smith et al. (“how accurate are climate simulations?”, Perspectives, 19 April, p. [483][1]) suggest that today's climate models simulate the climate history of Earth over the past 150 years “within the observed uncertainty of the observations.” In comparing model results with trends in sea surface temperature in several ocean basins, they ...
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Modeling uncertainty in databases

[1991] Proceedings. Seventh International Conference on Data Engineering, 2002
Relational algebra operations were extended to produce, together with answers to queries, information regarding sources that contributed to the answers. The author's previous model is reviewed and the semantic interpretation is presented. It is shown that extended relational algebra operations are precise, that is, they produce exactly the same answers
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A Fresh Perspective on Uncertainty Modeling: Uncertainty Vs. Uncertainty Modeling

1998
It is argued that very often when talking about the uncertainty of a system people confuse the phenomena with the glasses (theories) which they use to observe or model the uncertain phenomenon. Some experts also claim, that there is only one valid theory or tool (f. i. probability theory) to model all kinds of uncertainty. In this paper it is suggested,
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Modelling Uncertainty in Persuasion

2013
Participants in argumentation often have some doubts in their arguments and/or the arguments of the other participants. In this paper, we model uncertainty in beliefs using a probability distribution over models of the language, and use this to identify which are good arguments (i.e. those with support with a probability on or above a threshold).
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