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Subsidies under uncertainty: Modeling of input- and output-oriented policies
, 2020Agricultural subsidies play an essential role in agricultural and rural development in many developed economies. Countries have implemented agricultural subsidy policies with a focus on food security and environmental protection.
You-hua Chen, Mei-xia Chen, A. Mishra
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Big-data clustering with interval type-2 fuzzy uncertainty modeling in gene expression datasets
Engineering applications of artificial intelligence, 2019The major bottleneck in microarray gene expression analysis is the lack of techniques required to cope up with the uncertain gene functionality and inherent complex gene interactions.
Amit K. Shukla, Pranab K. Muhuri
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Modeling load forecast uncertainty using generative adversarial networks
Electric power systems research, 2020The integration of distributed energy resources (DER) increase the uncertainty of the load. Probabilistic load forecasting (PLF) is able to model these uncertainties in the form of quantile, interval, or density.
Yi Wang, G. Hug, Zijie Liu, Ning Zhang
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Sensitivity of Hosting Capacity to Data Resolution and Uncertainty Modeling
Australasian Universities Power Engineering Conference, 2018Integration limits of distributed generations (DGs) in distribution networks, i.e. the hosting capacity (HC), are highly dependent on uncertainties associated with the size, location and output power of DGs.
Mohammad Seydali Seyf Abad +4 more
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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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Musolff, R., Zimmermann, F.
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A Fresh Perspective on Uncertainty Modeling: Uncertainty Vs. Uncertainty Modeling
1998It 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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Predictive Uncertainty Estimation via Prior Networks
Neural Information Processing Systems, 2018Estimating how uncertain an AI system is in its predictions is important to improve the safety of such systems. Uncertainty in predictive can result from uncertainty in model parameters, irreducible data uncertainty and uncertainty due to distributional ...
A. Malinin, M. Gales
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Self-feature Distillation with Uncertainty Modeling for Degraded Image Recognition
European Conference on Computer Vision, 2022Zhou Yang +5 more
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MODEL UNCERTAINTY AND SCENARIO AGGREGATION
Mathematical Finance, 2014This 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, Filipović, Damir
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2019
Abstract This chapter covers model selection methods and model averaging methods. It relies on knowledge of solving a quadratic program which is outlined in an appendix.
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Abstract This chapter covers model selection methods and model averaging methods. It relies on knowledge of solving a quadratic program which is outlined in an appendix.
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