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Causal network reconstruction from time series: From theoretical assumptions to practical estimation

Runge, Jakob (2018) Causal network reconstruction from time series: From theoretical assumptions to practical estimation. Chaos, 28 (7), 075310. American Institute of Physics (AIP). ISSN 1054-1500

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Official URL: https://aip.scitation.org/doi/10.1063/1.5025050


Item URL in elib:https://elib.dlr.de/126424/
Document Type:Article
Title:Causal network reconstruction from time series: From theoretical assumptions to practical estimation
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Runge, JakobInstitute of Data Sciencehttps://orcid.org/0000-0002-0629-1772
Date:2018
Journal or Publication Title:Chaos
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:28
Page Range:075310
Publisher:American Institute of Physics (AIP)
ISSN:1054-1500
Status:Published
Keywords:causal inference
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
Deposited By: Runge, Jakob
Deposited On:08 Feb 2019 08:12
Last Modified:23 Feb 2019 00:23

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