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Numerical study of constraint-based time series causal discovery algorithms on synthetic data with heavy-tailed noise distributions

Gerhardus, Andreas and Runge, Jakob (2022) Numerical study of constraint-based time series causal discovery algorithms on synthetic data with heavy-tailed noise distributions. Workshop on Combining Causal Inference and Extreme Value Theory in the Study of Climate Extremes and their Causes, 2022-06-26 - 2022-07-01, Hybrid (Kelowna, Kanada, und virtuell).

Full text not available from this repository.

Official URL: http://www.birs.ca/events/2022/5-day-workshops/22w5079/videos/watch/202206290900-Gerhardus.html


Item URL in elib:https://elib.dlr.de/191030/
Document Type:Conference or Workshop Item (Speech)
Title:Numerical study of constraint-based time series causal discovery algorithms on synthetic data with heavy-tailed noise distributions
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Gerhardus, AndreasAndreas.Gerhardus (at) dlr.dehttps://orcid.org/0000-0003-1868-655XUNSPECIFIED
Runge, JakobJakob.Runge (at) dlr.dehttps://orcid.org/0000-0002-0629-1772UNSPECIFIED
Date:June 2022
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:causal discovery, causality, time series, extreme values, heavy tails
Event Title:Workshop on Combining Causal Inference and Extreme Value Theory in the Study of Climate Extremes and their Causes
Event Location:Hybrid (Kelowna, Kanada, und virtuell)
Event Type:Workshop
Event Start Date:26 June 2022
Event End Date:1 July 2022
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:other
DLR - Research area:Raumfahrt
DLR - Program:R - no assignment
DLR - Research theme (Project):R - no assignment
Location: Jena
Institutes and Institutions:Institute of Data Science > Data Analysis and Intelligence
Deposited By: Gerhardus, Andreas
Deposited On:05 Dec 2022 11:15
Last Modified:31 May 2024 11:12

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