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Modelling Uncertainty in Persuasion
2013Participants 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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Modeling Uncertainty in Banking Networks
2014Recent evidence from simulations of banking networks suggests that properties of the network design such as connectivity, bank size, or concentration affect networks’ ability to withstand stress (Arinaminpathy et al. 2012; Gai at al. 2011). However, those studies typically assume that all banks have complete knowledge about the whole system.
Stojan Davidovic +3 more
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Modelling positioning uncertainties
1989Robot programs generated by a human or by an automatic planner, must execute in a real environment which differs slightly from the model used at programming time. Thus we need to represent this uncertainty. This representation can be used at programming time to directly produce a valid program, or to verify the validity of a program afterwards.
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Uncertainty and Uncertainty Propagation in Soil Mapping and Modelling
2018In previous chapters, the use of geostatistical modelling for soil mapping was addressed. We learnt that one of the advantages of kriging is that it not only produces a map of predictions but that it also quantifies the uncertainty about the predictions, through the kriging standard deviation. In this chapter we will look into this in more detail.
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Stochastic Model Updating with Uncertainty Quantification: An Overview and Tutorial
Mechanical Systems and Signal Processing, 2023Sifeng Bi, Michael Beer, Scott Cogan
exaly
A Bayesian approach for quantification of model uncertainty
Reliability Engineering and System Safety, 2010Ramana V Grandhi
exaly