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of 3 595
pro vyhledávání: '"Gerhardus, A"'
A structural vector autoregressive (SVAR) process is a linear causal model for variables that evolve over a discrete set of time points and between which there may be lagged and instantaneous effects. The qualitative causal structure of an SVAR proce
Externí odkaz:
http://arxiv.org/abs/2406.17422
Autor:
Bujnáková, Dominika, Lansink, Gerhardus M. J., Abramov, Alexei V., Bulyonkova, Tatiana, Dokuchaev, Nikolai E., Domanov, Trofim, Dvornikov, Mikhail G., Graphodatsky, Alexander, Karabanina, Ekaterina, Kliver, Sergei, Korolev, Andrey N., Kozhechkin, Vladimir V., Litvinov, Yuri N., Mamaev, Nikolay, Monakhov, Vladimir G., Nanova, Olga, Okhlopkov, Innokentiy, Saveljev, Alexander P., Schinov, Anton, Shiriaeva, Elena, Sidorov, Mikhail, Tirronen, Konstantin F., Zakharov, Evgenii S., Zakharova, Nadezhda N., Aspi, Jouni, Kvist, Laura
Publikováno v:
Diversity and Distributions, 2024 Jul 01. 30(7), 1-18.
Externí odkaz:
https://www.jstor.org/stable/48777287
Conditional independence testing (CIT) is a common task in machine learning, e.g., for variable selection, and a main component of constraint-based causal discovery. While most current CIT approaches assume that all variables are numerical or all var
Externí odkaz:
http://arxiv.org/abs/2310.11132
In recent years, a growing number of method and application works have adapted and applied the causal-graphical-model framework to time series data. Many of these works employ time-resolved causal graphs that extend infinitely into the past and futur
Externí odkaz:
http://arxiv.org/abs/2310.05526
Autor:
Imke Schilling, Ansgar Gerhardus
Publikováno v:
BMC Medical Research Methodology, Vol 24, Iss 1, Pp 1-12 (2024)
Abstract Background There has been a growing push to involve patients in clinical research, shifting from conducting research on, about, or for them to conducting it with them. Two arguments advocate for this approach, known as Patient and Public Inv
Externí odkaz:
https://doaj.org/article/2d19cfa1a5dc4cfc9265192c90b8e56f
Publikováno v:
Proceedings of the Third Conference on Causal Learning and Reasoning, PMLR 236:979-1007, 2024
Learning causal graphs from multivariate time series is a ubiquitous challenge in all application domains dealing with time-dependent systems, such as in Earth sciences, biology, or engineering, to name a few. Recent developments for this causal disc
Externí odkaz:
http://arxiv.org/abs/2306.08946
Autor:
Camps-Valls, Gustau, Gerhardus, Andreas, Ninad, Urmi, Varando, Gherardo, Martius, Georg, Balaguer-Ballester, Emili, Vinuesa, Ricardo, Diaz, Emiliano, Zanna, Laure, Runge, Jakob
Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the phenomena. Discovering equations, laws and principles that are invarian
Externí odkaz:
http://arxiv.org/abs/2305.13341
When dealing with time series data, causal inference methods often employ structural vector autoregressive (SVAR) processes to model time-evolving random systems. In this work, we rephrase recursive SVAR processes with possible latent component proce
Externí odkaz:
http://arxiv.org/abs/2305.11561
Publikováno v:
Journal of Financial Crime, 2023, Vol. 31, Issue 4, pp. 810-822.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JFC-05-2023-0125
Autor:
S., Saranya Ganesh, Beucler, Tom, Tam, Frederick Iat-Hin, Gomez, Milton S., Runge, Jakob, Gerhardus, Andreas
Robust feature selection is vital for creating reliable and interpretable Machine Learning (ML) models. When designing statistical prediction models in cases where domain knowledge is limited and underlying interactions are unknown, choosing the opti
Externí odkaz:
http://arxiv.org/abs/2304.05294