Foundational Aspects for Incorporating Dependencies in Copula-Based Bayesian Networks Using Structured Expert Judgments, Exemplified by the Ice Sheet–Sea Level Rise Elicitation
The work presented here marks a further advance in expert uncertainty quantification. In a recent probabilistic evaluation of ice sheet process contributions to sea level rise, tail dependence was elicited and propagated through an uncertainty analysis for the first time. The elicited correlations a...
Published in: | Entropy |
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Main Authors: | , , |
Format: | Text |
Language: | English |
Published: |
Multidisciplinary Digital Publishing Institute
2024
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Subjects: | |
Online Access: | https://doi.org/10.3390/e26110949 |
_version_ | 1821548873908224000 |
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author | Dorota Kurowicka Willy Aspinall Roger Cooke |
author_facet | Dorota Kurowicka Willy Aspinall Roger Cooke |
author_sort | Dorota Kurowicka |
collection | MDPI Open Access Publishing |
container_issue | 11 |
container_start_page | 949 |
container_title | Entropy |
container_volume | 26 |
description | The work presented here marks a further advance in expert uncertainty quantification. In a recent probabilistic evaluation of ice sheet process contributions to sea level rise, tail dependence was elicited and propagated through an uncertainty analysis for the first time. The elicited correlations and tail dependencies concerned pairings of three processes: Accumulation, Discharge and Run-off, which operate on major ice sheets in the West and East Antarctic and in Greenland. The elicitation enumerated dependencies between these processes under selected global temperature change scenarios over different future time horizons. These expert judgments allowed us to populate a Paired Copula Bayesian network model to obtain the estimated contributions of these ice sheets for future sea level rise. Including positive central tendency dependence and tail dependence increases the fatness of the upper tails of projected sea level rise distributions, an amplification important for designing and evaluating possible mitigation strategies. Detailing and jointly computing distributional dependencies and tail dependencies can be crucial components of good practice for assessing the influence of uncertainties on extreme values when modelling stochastic multifactorial processes. |
format | Text |
genre | Antarc* Antarctic Greenland Ice Sheet |
genre_facet | Antarc* Antarctic Greenland Ice Sheet |
geographic | Antarctic Greenland |
geographic_facet | Antarctic Greenland |
id | ftmdpi:oai:mdpi.com:/1099-4300/26/11/949/ |
institution | Open Polar |
language | English |
op_collection_id | ftmdpi |
op_doi | https://doi.org/10.3390/e26110949 |
op_relation | Multidisciplinary Applications https://dx.doi.org/10.3390/e26110949 |
op_rights | https://creativecommons.org/licenses/by/4.0/ |
op_source | Entropy Volume 26 Issue 11 Pages: 949 |
publishDate | 2024 |
publisher | Multidisciplinary Digital Publishing Institute |
record_format | openpolar |
spelling | ftmdpi:oai:mdpi.com:/1099-4300/26/11/949/ 2025-01-16T19:04:25+00:00 Foundational Aspects for Incorporating Dependencies in Copula-Based Bayesian Networks Using Structured Expert Judgments, Exemplified by the Ice Sheet–Sea Level Rise Elicitation Dorota Kurowicka Willy Aspinall Roger Cooke 2024-11-05 application/pdf https://doi.org/10.3390/e26110949 eng eng Multidisciplinary Digital Publishing Institute Multidisciplinary Applications https://dx.doi.org/10.3390/e26110949 https://creativecommons.org/licenses/by/4.0/ Entropy Volume 26 Issue 11 Pages: 949 expert judgment Bayesian networks copulas Text 2024 ftmdpi https://doi.org/10.3390/e26110949 2024-11-08T01:07:28Z The work presented here marks a further advance in expert uncertainty quantification. In a recent probabilistic evaluation of ice sheet process contributions to sea level rise, tail dependence was elicited and propagated through an uncertainty analysis for the first time. The elicited correlations and tail dependencies concerned pairings of three processes: Accumulation, Discharge and Run-off, which operate on major ice sheets in the West and East Antarctic and in Greenland. The elicitation enumerated dependencies between these processes under selected global temperature change scenarios over different future time horizons. These expert judgments allowed us to populate a Paired Copula Bayesian network model to obtain the estimated contributions of these ice sheets for future sea level rise. Including positive central tendency dependence and tail dependence increases the fatness of the upper tails of projected sea level rise distributions, an amplification important for designing and evaluating possible mitigation strategies. Detailing and jointly computing distributional dependencies and tail dependencies can be crucial components of good practice for assessing the influence of uncertainties on extreme values when modelling stochastic multifactorial processes. Text Antarc* Antarctic Greenland Ice Sheet MDPI Open Access Publishing Antarctic Greenland Entropy 26 11 949 |
spellingShingle | expert judgment Bayesian networks copulas Dorota Kurowicka Willy Aspinall Roger Cooke Foundational Aspects for Incorporating Dependencies in Copula-Based Bayesian Networks Using Structured Expert Judgments, Exemplified by the Ice Sheet–Sea Level Rise Elicitation |
title | Foundational Aspects for Incorporating Dependencies in Copula-Based Bayesian Networks Using Structured Expert Judgments, Exemplified by the Ice Sheet–Sea Level Rise Elicitation |
title_full | Foundational Aspects for Incorporating Dependencies in Copula-Based Bayesian Networks Using Structured Expert Judgments, Exemplified by the Ice Sheet–Sea Level Rise Elicitation |
title_fullStr | Foundational Aspects for Incorporating Dependencies in Copula-Based Bayesian Networks Using Structured Expert Judgments, Exemplified by the Ice Sheet–Sea Level Rise Elicitation |
title_full_unstemmed | Foundational Aspects for Incorporating Dependencies in Copula-Based Bayesian Networks Using Structured Expert Judgments, Exemplified by the Ice Sheet–Sea Level Rise Elicitation |
title_short | Foundational Aspects for Incorporating Dependencies in Copula-Based Bayesian Networks Using Structured Expert Judgments, Exemplified by the Ice Sheet–Sea Level Rise Elicitation |
title_sort | foundational aspects for incorporating dependencies in copula-based bayesian networks using structured expert judgments, exemplified by the ice sheet–sea level rise elicitation |
topic | expert judgment Bayesian networks copulas |
topic_facet | expert judgment Bayesian networks copulas |
url | https://doi.org/10.3390/e26110949 |