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An analysis of the free modes of one, two and four layer models based on sigma coordinates

CITE Title : An analysis of the free modes of one, two and four layer models based on sigma coordinates Personal Author(s) : Gerrity, Joseph P. Corporate Authors(s) : National Meteorological Center (U.S.) Published Date : 1971 Series : Office note (National Meteorological Center (U.S.)) ; 47 URL : /view/noaa/12322 Gerrity, Joseph P. (1971). An analysis of the free modes of one, two and four layer models based on sigma coordinates. Gerrity, Joseph P. "An analysis of the free modes of one, two and four layer models based on sigma coordinates" (1971) Gerrity, Joseph P. "An analysis of the free modes of one, two and four layer models based on sigma coordinates" , 1971 Export RIS Citation Information.

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Gerrity, Joseph P.

1971 | Office note (National Meteorological Center (U.S.)) ; 47

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"It is well-known that the computational stability of explicit schemes for numerical integration of the quasi-static equations is dependent upon the s...
Greater Than the Sum of its Parts: Computationally Flexible Bayesian Hierarchical Modeling

CITE Title : Greater Than the Sum of its Parts: Computationally Flexible Bayesian Hierarchical Modeling Personal Author(s) : Johnson, Devin S.;Brost, Brian M.;Hooten, Mevin B.; Published Date : 2022 Source : Journal of Agricultural, Biological and Environmental Statistics, 27(2), 382-400 DOI : https://doi.org/10.1007/s13253-021-00485-9 Journal Title : Journal of Agricultural, Biological and Environmental Statistics Johnson, Devin S. et al. (2022). Greater Than the Sum of its Parts: Computationally Flexible Bayesian Hierarchical Modeling. 27(2). https://doi.org/10.1007/s13253-021-00485-9 Johnson, Devin S. and Brost, Brian M. and Hooten, Mevin B. "Greater Than the Sum of its Parts: Computationally Flexible Bayesian Hierarchical Modeling" 27, no. 2 (2022), https://doi.org/10.1007/s13253-021-00485-9 Johnson, Devin S. et al. "Greater Than the Sum of its Parts: Computationally Flexible Bayesian Hierarchical Modeling" vol. 27, no. 2, 2022, https://doi.org/10.1007/s13253-021-00485-9 Export RIS Citation Information.

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Johnson, Devin S.

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Brost, Brian M.

2022 | Journal of Agricultural, Biological and Environmental Statistics, 27(2), 382-400

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We propose a multistage method for making inference at all levels of a Bayesian hierarchical model (BHM) using natural data partitions to increase eff...
Modeling a very rare event to estimate sea turtle bycatch lessons learned

CITE Title : Modeling a very rare event to estimate sea turtle bycatch lessons learned Personal Author(s) : McCracken, Marti L. Corporate Authors(s) : Pacific Islands Fisheries Science Center (U.S.) Published Date : 2004 Series : NOAA technical memorandum NMFS-PIFSC ; 3 URL : /view/noaa/3399 McCracken, Marti L. (2004). Modeling a very rare event to estimate sea turtle bycatch lessons learned. McCracken, Marti L. "Modeling a very rare event to estimate sea turtle bycatch lessons learned" (2004) McCracken, Marti L. "Modeling a very rare event to estimate sea turtle bycatch lessons learned" , 2004 Export RIS Citation Information.

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McCracken, Marti L.

2004 | NOAA technical memorandum NMFS-PIFSC ; 3

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