Zobrazeno 1 - 10
of 3 628
pro vyhledávání: '"Gerhardus, A."'
Autor:
Günther, Wiebke, Popescu, Oana-Iuliana, Rabel, Martin, Ninad, Urmi, Gerhardus, Andreas, Runge, Jakob
Causal systems often exhibit variations of the underlying causal mechanisms between the variables of the system. Often, these changes are driven by different environments or internal states in which the system operates, and we refer to context variab
Externí odkaz:
http://arxiv.org/abs/2412.04981
Causal structure learning with data from multiple contexts carries both opportunities and challenges. Opportunities arise from considering shared and context-specific causal graphs enabling to generalize and transfer causal knowledge across contexts.
Externí odkaz:
http://arxiv.org/abs/2410.20405
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:
Simone Böbel, Ansgar Gerhardus, Carolin Herbon, Hannah Jilani, Kim Isabel Rathjen, Guido Schmiemann, Imke Schilling
Publikováno v:
Research Involvement and Engagement, Vol 10, Iss 1, Pp 1-13 (2024)
Abstract Background Patient and Public Involvement (PPI) is increasingly recognized as an essential aspect of clinical research, particularly for ensuring relevancy and impact of research to those most affected. This study addresses the gap in involv
Externí odkaz:
https://doaj.org/article/66edb16995a047fc8aac9d58dd27da9f
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