Zobrazeno 1 - 10
of 130
pro vyhledávání: '"Chicharro Daniel"'
Publikováno v:
BMC Neuroscience, Vol 12, Iss Suppl 1, p P3 (2011)
Externí odkaz:
https://doaj.org/article/41be7abe8d754a60bd49a3ea85a7f36b
Publikováno v:
BMC Neuroscience, Vol 12, Iss Suppl 1, p P192 (2011)
Externí odkaz:
https://doaj.org/article/faffef47683c43009d395ccee0fae7bf
Publikováno v:
BMC Neuroscience, Vol 10, Iss Suppl 1, p P271 (2009)
Externí odkaz:
https://doaj.org/article/cb3179c8963e44d495a36345e2716616
Autor:
Abarbanel Henry DI, Haas Julie S, Andrzejak Ralph G, Chicharro Daniel, Kreuz Thomas, Torcini Alessandro, Politi Antonio
Publikováno v:
BMC Neuroscience, Vol 9, Iss Suppl 1, p P30 (2008)
Externí odkaz:
https://doaj.org/article/ceb1b9e069a7435c9596c1a980594206
Causal Structure Learning with Conditional and Unique Information Groups-Decomposition Inequalities.
Autor:
Chicharro, Daniel1 (AUTHOR) chicharro31@yahoo.es, Nguyen, Julia K.2 (AUTHOR)
Publikováno v:
Entropy. Jun2024, Vol. 26 Issue 6, p440. 34p.
The inference of causal relationships using observational data from partially observed multivariate systems with hidden variables is a fundamental question in many scientific domains. Methods extracting causal information from conditional independenc
Externí odkaz:
http://arxiv.org/abs/2010.05375
Constraint-based structure learning algorithms infer the causal structure of multivariate systems from observational data by determining an equivalent class of causal structures compatible with the conditional independencies in the data. Methods base
Externí odkaz:
http://arxiv.org/abs/1905.08360
Publikováno v:
Entropy 21 (9), 862, 2019
Chicharro (2017) introduced a procedure to determine multivariate partial information measures within the maximum entropy framework, separating unique, redundant, and synergistic components of information. Makkeh, Theis, and Vicente (2018) formulated
Externí odkaz:
http://arxiv.org/abs/1901.03352
Understanding how different information sources together transmit information is crucial in many domains. For example, understanding the neural code requires characterizing how different neurons contribute unique, redundant, or synergistic pieces of
Externí odkaz:
http://arxiv.org/abs/1711.11408
Autor:
Chicharro, Daniel
Williams and Beer (2010) proposed a nonnegative mutual information decomposition, based on the construction of redundancy lattices, which allows separating the information that a set of variables contains about a target variable into nonnegative comp
Externí odkaz:
http://arxiv.org/abs/1708.03845