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...

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Published in:Entropy
Main Authors: Dorota Kurowicka, Willy Aspinall, Roger Cooke
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2024
Subjects:
Online Access:https://doi.org/10.3390/e26110949
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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.
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Antarctic
Greenland
Ice Sheet
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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