Reliability of Inference of Directed Climate Networks Using Conditional Mutual Information

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F13%3A00393073" target="_blank" >RIV/67985807:_____/13:00393073 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.3390/e15062023" target="_blank" >http://dx.doi.org/10.3390/e15062023</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/e15062023" target="_blank" >10.3390/e15062023</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Reliability of Inference of Directed Climate Networks Using Conditional Mutual Information

Popis výsledku v původním jazyce

Across geosciences, many investigated phenomena relate to specific complex systems consisting of intricately intertwined interacting subsystems. Such dynamical complex systems can be represented by a directed graph, where each link denotes an existence of a causal relation, or information exchange between the nodes. For geophysical systems such as global climate, these relations are commonly not theoretically known but estimated from recorded data using causality analysis methods. These include bivariate nonlinear methods based on information theory and their linear counterpart. The trade-off between the valuable sensitivity of nonlinear methods to more general interactions and the potentially higher numerical reliability of linear methods may affect inference regarding structure and variability of climate networks. We investigate the reliability of directed climate networks detected by selected methods and parameter settings, using a stationarized model of dimensionality-reduced surfa

Název v anglickém jazyce

Reliability of Inference of Directed Climate Networks Using Conditional Mutual Information

Popis výsledku anglicky

Across geosciences, many investigated phenomena relate to specific complex systems consisting of intricately intertwined interacting subsystems. Such dynamical complex systems can be represented by a directed graph, where each link denotes an existence of a causal relation, or information exchange between the nodes. For geophysical systems such as global climate, these relations are commonly not theoretically known but estimated from recorded data using causality analysis methods. These include bivariate nonlinear methods based on information theory and their linear counterpart. The trade-off between the valuable sensitivity of nonlinear methods to more general interactions and the potentially higher numerical reliability of linear methods may affect inference regarding structure and variability of climate networks. We investigate the reliability of directed climate networks detected by selected methods and parameter settings, using a stationarized model of dimensionality-reduced surfa

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

<a href="/cs/project/GCP103%2F11%2FJ068" target="_blank" >GCP103/11/J068: Interakce, přenos informace a složité struktury v dynamice měnícího se klimatu</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Entropy

ISSN

1099-4300

e-ISSN

—

Svazek periodika

15

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

23

Strana od-do

2023-2045

Kód UT WoS článku

000320773000005

EID výsledku v databázi Scopus

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