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Uncertainty Modelling and Quality Control for Spatial Data

settlement Uncertainty Modelling and Quality Control for Spatial Data serves university students, researchers and professionals;

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Uncertainty Modelling and Quality Control for Spatial Data

settlement Uncertainty Modelling and Quality Control for Spatial Data serves university students, researchers and professionals;

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Uncertainty Analysis with High Dimensional Dependence Modelling

well as computer programs and data sets to support the examples. Uncertainty Analysis with High Dimensional Dependence Modelling offers a;

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Low-cost space-borne data for inundation modelling: topography, flood extent and water level

under uncertainty, including findings related to the usefulness of these data according to modelling purpose (e.g. re-insurance, planning;

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Low-cost space-borne data for inundation modelling: topography, flood extent and water level

under uncertainty, including findings related to the usefulness of these data according to modelling purpose (e.g. re-insurance, planning;

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Bridging the Geographic Information Sciences

and Geo Semantic Web; Modelling and Management of Uncertainty; Spatio-temporal Data Quality and Metadata; Mobility of Persons, Objects and;

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Advances in Geo-Spatial Information Science

and time, spatial analysis, uncertainty modeling and geo-visualization), and new scientific and technological research initiatives for geo;

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Environmental Modelling

of uncertainty the constraint of uncertainties by the collection of observations and data assimilation in real-time forecasting;

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Environmental Modelling

of uncertainty the constraint of uncertainties by the collection of observations and data assimilation in real-time forecasting;

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Computational Science ICCS 2021

; Biomedical and Bioinformatics Challenges for Computer Science Part III: Classifier Learning from Difficult Data; Computational Analysis;

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Uncertainty Modelling in Data Science

predictive models is the trust placed in them. Modelling the uncertainty associated with the data and the models carefully and with principled;

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Computational Science ICCS 2021

; Biomedical and Bioinformatics Challenges for Computer Science Part III: Classifier Learning from Difficult Data; Computational Analysis;

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Proceedings of the 5th International Symposium on Uncertainty Quantification and Stochastic Modelling

This proceedings book discusses state-of-the-art research on uncertainty quantification in mechanical engineering, including statistical;

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Modelling Under Risk and Uncertainty

description be, given the true data limitations ? Could the uncertainty be reduced through more data, increased modeling investment or computational;

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Uncertainty Modeling for Data Mining

Machine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise;

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Integrated Uncertainty in Knowledge Modelling and Decision Making

This book constitutes the refereed proceedings of the 7th International Symposium on Integrated Uncertainty in Knowledge Modelling and;

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Computational Science ICCS 2021

; Biomedical and Bioinformatics Challenges for Computer Science Part III: Classifier Learning from Difficult Data; Computational Analysis;

Vergelijkbare producten zoals Computational Science ICCS 2021

Computational Science ICCS 2021

; Biomedical and Bioinformatics Challenges for Computer Science Part III: Classifier Learning from Difficult Data; Computational Analysis;

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Integrated Uncertainty in Knowledge Modelling and Decision Making

This book constitutes the refereed proceedings of the 8th International Symposium on Integrated Uncertainty in Knowledge Modelling and;

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Integrated Uncertainty in Knowledge Modelling and Decision Making

This book constitutes the refereed proceedings of the 5th International Symposium on Integrated Uncertainty in Knowledge Modelling and;

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Reservoir Modelling

The essential resource to an integrated approach to reservoir modelling by highlighting both the input of data and the modelling results;

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Reservoir Model Design

cover rock architecture, petrophysical property modelling, multi-scale data integration, upscaling and uncertainty analysis. Philip Ringrose and;

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Applied modelling and computing in social science

in social science: business and logistic process simulation and optimization, deeper knowledge extractions from big data, better understanding and;

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Modelling and Simulation in Management Sciences

to fuzzy logic, soft computing and uncertainty, as well as a number of papers in the field of bibliometrics in social sciences. Presenting;

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Computational Science - ICCS 2020

for Computer Science Part IV: Classifier Learning from Difficult Data; Complex Social Systems through the Lens of Computational Science;

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Computational Science - ICCS 2020

Part IV: Classifier Learning from Difficult Data; Complex Social Systems through the Lens of Computational Science; Computational Health;

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Computational Science - ICCS 2020

for Computer Science Part IV: Classifier Learning from Difficult Data; Complex Social Systems through the Lens of Computational Science;

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