QUANTIFYING UNCERTAINTY: MODERN COMPUTATIONAL REPRESENTATION OF PROBABILITY AND APPLICATIONS

2007 ◽  
pp. 105-135 ◽  
Author(s):  
Hermann G. Matthies
Keyword(s):  
Computational Representation ◽  
Quantifying Uncertainty

Quantifying Uncertainty to Improve Decision Making in Machine Learning.

2018 ◽  
Author(s):  
David John Stracuzzi ◽  
Michael Christopher Darling ◽  
Matthew Gregor Peterson ◽  
Maximillian Gene Chen
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Quantifying Uncertainty

GraphBoot: Quantifying Uncertainty in Node Feature Learning on Large Networks

2021 ◽  
Vol 33 (1) ◽  
pp. 116-127 ◽  
Author(s):  
Cuneyt Gurcan Akcora ◽  
Yulia R. Gel ◽  
Murat Kantarcioglu ◽  
Vyacheslav Lyubchich ◽  
Bhavani Thuraisingham
Keyword(s):  
Feature Learning ◽  
Large Networks ◽  
Quantifying Uncertainty

scholarly journals Quantifying Uncertainty in Exposure to Coastal Hazards Associated with Both Climate Change and Adaptation Strategies: A U.S. Pacific Northwest Alternative Coastal Futures Analysis

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 545
Author(s):  
Alexis K. Mills ◽  
Peter Ruggiero ◽  
John P. Bolte ◽  
Katherine A. Serafin ◽  
Eva Lipiec
Keyword(s):  
Climate Change ◽  
Sea Level Rise ◽  
Sea Level ◽  
Performance Metrics ◽  
Coastal Flooding ◽  
Water Levels ◽  
Coastal Hazards ◽  
Policy Decisions ◽  
Management Decisions ◽  
Quantifying Uncertainty

Coastal communities face heightened risk to coastal flooding and erosion hazards due to sea-level rise, changing storminess patterns, and evolving human development pressures. Incorporating uncertainty associated with both climate change and the range of possible adaptation measures is essential for projecting the evolving exposure to coastal flooding and erosion, as well as associated community vulnerability through time. A spatially explicit agent-based modeling platform, that provides a scenario-based framework for examining interactions between human and natural systems across a landscape, was used in Tillamook County, OR (USA) to explore strategies that may reduce exposure to coastal hazards within the context of climate change. Probabilistic simulations of extreme water levels were used to assess the impacts of variable projections of sea-level rise and storminess both as individual climate drivers and under a range of integrated climate change scenarios through the end of the century. Additionally, policy drivers, modeled both as individual management decisions and as policies integrated within adaptation scenarios, captured variability in possible human response to increased hazards risk. The relative contribution of variability and uncertainty from both climate change and policy decisions was quantified using three stakeholder relevant landscape performance metrics related to flooding, erosion, and recreational beach accessibility. In general, policy decisions introduced greater variability and uncertainty to the impacts of coastal hazards than climate change uncertainty. Quantifying uncertainty across a suite of coproduced performance metrics can help determine the relative impact of management decisions on the adaptive capacity of communities under future climate scenarios.

Quantifying uncertainty in changes in extreme event frequency in response to doubled CO2 using a large ensemble of GCM simulations

2006 ◽  
Vol 26 (5) ◽  
pp. 489-511 ◽  
Author(s):  
David N. Barnett ◽  
Simon J. Brown ◽  
James M. Murphy ◽  
David M. H. Sexton ◽  
Mark J. Webb
Keyword(s):  
Extreme Event ◽  
Large Ensemble ◽  
Event Frequency ◽  
Quantifying Uncertainty

Quantifying Uncertainty

2019 ◽  
pp. 249-262
Author(s):  
Walker H. Land ◽  
J. David Schaffer
Keyword(s):  
Quantifying Uncertainty
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