Výsledky vyhledávání - "Catharina Elisabeth Graafland"

Global wildfire synchronicity patterns as revealed by complex network analysis

Autor: Joaquin Bedia, Catharina Elisabeth Graafland, Andrina Gincheva, Marco Turco

In light of the ongoing global climate change, a better understanding of global wildfire activity is the key to anticipate future impacts and minimize, as much as possible, their negative consequences on natural ecosystems and human economy. In this

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::fe996ebea74015e5a73cb67bdcc7833d
https://doi.org/10.5194/egusphere-egu23-9608

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Assessing model dependency in CMIP5 and CMIP6 based on their spatial dependency structure with probabilistic network models

Autor: Catharina Elisabeth Graafland, Jose Manuel Gutiérrez Gutiérrez

Probabilistic network models (PNMs) are well established data-driven modeling and machine learning prediction techniques used in many disciplines, including climate analysis. These techniques can efficiently learn the underlying (spatial) dependency

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b8a1fe4533be080408c72b94485a44f6
https://doi.org/10.5194/egusphere-egu22-12822

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The probabilistic backbone of data-driven complex networks: an example in climate

Autor: Diego Pazó, José M. Gutiérrez, Miguel A. Rodríguez, Catharina Elisabeth Graafland, Juan M. López

Publikováno v: Scientific Reports, Vol 10, Iss 1, Pp 1-15 (2020)
Scientific Reports
Digital.CSIC. Repositorio Institucional del CSIC
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Complex systems often exhibit long-range correlations so that typical observables show statistical dependence across long distances. These teleconnections have a tremendous impact on the dynamics as they provide channels for information transport acr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1b7a65c24d8e217e45c51fb9a0309538
http://link.springer.com/article/10.1038/s41598-020-67970-y

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Who learns better Bayesian network structures: Accuracy and speed of structure learning algorithms

Autor: José M. Gutiérrez, Catharina Elisabeth Graafland, Marco Scutari

Publikováno v: Digital.CSIC. Repositorio Institucional del CSIC
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Three classes of algorithms to learn the structure of Bayesian networks from data are common in the literature: constraint-based algorithms, which use conditional independence tests to learn the dependence structure of the data; score-based algorithm

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::422929491abc1fb9a534ff273bba4124
http://hdl.handle.net/10261/213670

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Unveiling global wildifre sinchronicity patterns using complex networks

Autor: Gracia Borobia, Sergio

Trabajo fin de Máster defendido en la Facultad de Ciencias de la Universidad de Cantabria, el 15 de julio de 2022 -Curso 2021-2022 - Máster Interuniversitario en Ciencia de Datos / Master in Data Science (UIMP-UC-CSIC)
[EN] In light of the ong

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______1106::284f5ba167446b55cbe8f7d23a31d2dc
http://hdl.handle.net/10261/280877

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