Profile Extrema for Visualizing and Quantifying Uncertainties on Excursion Regions: Application to Coastal Flooding-Reference-Cited by-同舟云学术

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Profile Extrema for Visualizing and Quantifying Uncertainties on Excursion Regions: Application to Coastal Flooding

Published:2019-05-13 Issue:4 Volume:61 Page:474-493
ISSN:0040-1706
Container-title:Technometrics
language:en
Short-container-title:Technometrics

Author:

Azzimonti Dario¹^ORCID,Ginsbourger David²³^ORCID,Rohmer Jérémy⁴^ORCID,Idier Déborah⁴^ORCID

Affiliation:

1. Istituto Dalle Molle di studi sull’Intelligenza Artificiale (IDSIA), Manno, Switzerland;

2. UQOD, Idiap Research Institute Centre du Parc, Martigny, Switzerland;

3. IMSV, Department of Mathematics and Statistics, University of Bern, Bern, Switzerland;

4. BRGM, Orléans Cedex, France

Funder

Hasler Foundation

Publisher

Informa UK Limited

Subject

Applied Mathematics,Modeling and Simulation,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/00401706.2018.1562987

Reference36 articles.

1. The Grand Tour: A Tool for Viewing Multidimensional Data

2. Level Sets and Extrema of Random Processes and Fields

3. Azzimonti, D. (2016), “Contributions to Bayesian Set Estimation Relying on Random Field Priors,” PhD thesis, University of Bern.

4. Quantifying Uncertainties on Excursion Sets Under a Gaussian Random Field Prior

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1. Sequential Construction and Dimension Reduction of Gaussian Processes Under Inequality Constraints;SIAM Journal on Mathematics of Data Science;2022-06

2. Multioutput Gaussian processes with functional data: A study on coastal flood hazard assessment;Reliability Engineering & System Safety;2022-02

3. Targeted Reduction of p-Boxes in Risk Assessments With Mixed Aleatory and Epistemic Uncertainties;ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg;2021-04-23

4. Coastal flood: a composite method for past events characterisation providing insights in past, present and future hazards—joining historical, statistical and modelling approaches;Natural Hazards;2020-02-22

5. Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC;Springer Proceedings in Mathematics & Statistics;2020

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