A learning algorithm for source aggregation

Papayiannis, Georgios I.; Yannacopoulos, A. N.

doi:10.1002/mma.4086

Cited by 10 publications

(12 citation statements)

References 12 publications

(23 reference statements)

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“…where θ := (r −λ(1−β))/β. Then, substituting (34) to ( 22) we obtain the stated result (19) in Proposition 1. Also, combining equations ( 25) and ( 34) and substituting to (21) are obtained the optimal controls stated in (20) in Proposition 1.…”

Section: 1mentioning

confidence: 84%

“…Lastly, the issue of model validity is something that someone has to be aware of, especially in cases where the decision making process is performed under a dynamic setting and possibly during the process new evidence become available. In this direction, learning schemes that determine the optimal weight allocation to each model, taking into account criteria assessing the models' performance comparing to the recorded data, are proposed and investigated under different metric tools in [19].…”

mentioning

confidence: 99%

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Robust policy selection and harvest risk quantification for natural resources management under model uncertainty

Papayiannis

2022

JDG

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<p style='text-indent:20px;'>In this work the problem of optimal harvesting policy selection for natural resources management under model uncertainty is investigated. Under the framework of the neoclassical growth model dynamics, the associated optimal control problem is investigated by introducing the concept of model uncertainty on the initial conditions of the operational procedure. At this stage, the notion of convex risk measures, and in particular the class of Fréchet risk measures, is employed in order to quantify the total operational and marginal risk, whereas simultaneously obtaining robust to model uncertainty harvesting strategies.</p>

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Section: 1mentioning

confidence: 84%

mentioning

confidence: 99%

Robust policy selection and harvest risk quantification for natural resources management under model uncertainty

Papayiannis

2022

JDG

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“…As points on this space corresponds to probability measures, the Wasserstein barycenter of a collection of probability measures

scriptM

corresponds to the best approximation of the entire collection by a single probability measure. Wasserstein barycenters are a very active field of research recently and have been studied from various perspectives (see, e.g., Agueh & Carlier, ; Kim & Pass, ; Le Gouic & Loubes, ) including statistical learning (Papayiannis & Yannacopoulos, ).…”

Section: Weight Selection Approaches For the Aggregate Modelsmentioning

confidence: 99%

“…In this section, we consider a learning scheme for the optimal weight selection of the predictions aggregate model that can be considered as an extension of the algorithm presented in the previous Section 3.2, relying on the algorithmic approach proposed in the work of Papayiannis and Yannacopoulos (). The proposed scheme relies on the superior performance of the Wasserstein distance when used as a loss/merit function in the space of probability measures.…”

Section: Weight Selection Approaches For the Aggregate Modelsmentioning

confidence: 99%

“…As we already mentioned in Section 3.2, the Wasserstein distance is a true metric in the space of probability measures (see also Santambrogio, ; Villani, , for justification on this argument) and, as such, is a very appropriate tool for the efficient measurement of discrepancies between probability distributions. Additionally, in the work of Papayiannis and Yannacopoulos (), certain aspects of the superior performance of the Wasserstein metric, as compared with other well‐known distance functionals used in practice when quantifying distance between probability distributions, have been reported for a wide class of distributions.…”

Section: Weight Selection Approaches For the Aggregate Modelsmentioning

confidence: 99%

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Model aggregation using optimal transport and applications in wind speed forecasting

2018

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In several environmental affected socio‐economic activities, including renewable energy site assessment, search and rescue operations, and local microclimate modeling, the need of very local wind speed prediction is critical and not completely covered by the use of numerical weather prediction models. In meteorology and, particularly, in wind speed prediction, the spatial location of the prediction does not coincide with the spatial locations where numerical models provide estimates of the relevant quantity (which are typically grid points used for the numerical resolution of the wind transport equations). Hence, the important problem of constructing a predictive model for the wind speed at the required location using a combination of actual measurements and model predictions arises. This problem is far from trivial on account of the fact that measurements and predictions do not refer to the same quantity for the reason that typical grid points for the numerical scheme that provide model predictions and the location of the meteorological stations that provide measurements do not coincide. In this work, a new approach is proposed based on optimal transportation theory for the aggregation of model predictions and measurements for the construction of an optimal predictor for wind speed at the location of interest. Our model provides a linear predictive model in the space of probability distributions of the predictors (Wasserstein space), which is then mapped into observation space using a generalized quantile regression technique. Importantly, the proposed scheme allows also for the construction of zone monitoring the extremes, which when applied to real data, provides superior results with respect to other existing methods.

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Consensus Group Decision Making Under Model Uncertainty with a View Towards Environmental Policy Making

Koundouri,

Papayiannis,

Petracou

et al. 2024

Environ Resource Econ

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A learning algorithm for source aggregation

Cited by 10 publications

References 12 publications

Robust policy selection and harvest risk quantification for natural resources management under model uncertainty

Robust policy selection and harvest risk quantification for natural resources management under model uncertainty

Model aggregation using optimal transport and applications in wind speed forecasting

Consensus Group Decision Making Under Model Uncertainty with a View Towards Environmental Policy Making

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