Relevant bibliographies by topics / Diagramme Causaux

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Academic literature on the topic 'Diagramme Causaux'

Author: Grafiati
Published: 7 July 2024
Last updated: 7 July 2024

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Journal articles on the topic "Diagramme Causaux"

1

Nicholls, Jim, and J. Kelly Russell. "Igneous Rock Associations 20. Pearce Element Ratio Diagrams: Linking Geochemical Data to Magmatic Processes." Geoscience Canada 43, no. 2 (2016): 133. http://dx.doi.org/10.12789/geocanj.2016.43.095.

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It has been nearly fifty years since Tom Pearce devised a type of element ratio diagram that isolates the effects of crystal fractionation and accumulation (sorting) hidden in the chemistry of a suite of igneous rocks. Here, we review the guiding principles and methods supporting the Pearce element ratio paradigm and provide worked examples with data from the Mauna Ulu lava flows (erupted 1970–1971, Kilauea Volcano, Hawaii). Construction of Pearce element ratio diagrams requires minimum data; a single rock analysis can suffice. The remaining data test the model. If the data fit the model, then
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2

Tipán, Luis, and Juan Carlos Muela. "Simulación causal para el consumo eléctrico residencial." Revista Técnica "energía" 17, no. 1 (2020): 60–70. http://dx.doi.org/10.37116/revistaenergia.v17.n1.2020.384.

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Un modelo dinámico basado en diagramas causales pretende identificar todos los factores involucrados en el comportamiento de un fenómeno especifico. El presente artículo implementa un modelo dinámico basado en diagramas causales que busca identificar el comportamiento y respectivo consumo eléctrico residencial. La metodología aplicada involucra variables aleatorias que buscan replicar el comportamiento estocástico al interior de una vivienda durante el día, la intención de aplicar un modelo causal radica en la interacción y dependencia existente entre variables, es decir el condicionamiento qu
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3

Schisterman, Enrique F., Neil J. Perkins, Sunni L. Mumford, Katherine A. Ahrens, and Emily M. Mitchell. "Collinearity and Causal Diagrams." Epidemiology 28, no. 1 (2017): 47–53. http://dx.doi.org/10.1097/ede.0000000000000554.

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4

Ogburn, Elizabeth L., and Tyler J. VanderWeele. "Causal Diagrams for Interference." Statistical Science 29, no. 4 (2014): 559–78. http://dx.doi.org/10.1214/14-sts501.

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5

Suzuki, Etsuji, Tomohiro Shinozaki, and Eiji Yamamoto. "Causal Diagrams: Pitfalls and Tips." Journal of Epidemiology 30, no. 4 (2020): 153–62. http://dx.doi.org/10.2188/jea.je20190192.

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6

Picciotto*, Sally. "Causal Diagrams and Their Uses." ISEE Conference Abstracts 2014, no. 1 (2014): 2901. http://dx.doi.org/10.1289/isee.2014.s-063.

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7

Mansournia, Mohammad A., Miguel A. Hernán, and Sander Greenland. "Matched designs and causal diagrams." International Journal of Epidemiology 42, no. 3 (2013): 860–69. http://dx.doi.org/10.1093/ije/dyt083.

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8

Greenland, Sander, Judea Pearl, and James M. Robins. "Causal Diagrams for Epidemiologic Research." Epidemiology 10, no. 1 (1999): 37–48. http://dx.doi.org/10.1097/00001648-199901000-00008.

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9

PEARL, JUDEA. "Causal diagrams for empirical research." Biometrika 82, no. 4 (1995): 669–88. http://dx.doi.org/10.1093/biomet/82.4.669.

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10

COX, D. R., and NANNY WERMUTH. "Causal diagrams for empirical research." Biometrika 82, no. 4 (1995): 688–89. http://dx.doi.org/10.1093/biomet/82.4.688.

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