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1
Parallel Approximations of the Tukey g-and-h Likelihoods an..:
, In:
2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
,
Mondal, Sagnik
;
Abdulah, Sameh
;
Ltaief, Hatem
... - p. 379-389 , 2022
Link:
https://doi.org/10.1109/IPDPS53621.2022.00044
RT T1 <cTitle>2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)</cTitle>: T1 <Title>Parallel Approximations of the Tukey g-and-h Likelihoods and Predictions for Non-Gaussian Geostatistics</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-9820617&Exemplar=1&LAN=DE A1 Mondal, Sagnik A1 Abdulah, Sameh A1 Ltaief, Hatem A1 Sun, Ying A1 Genton, Marc G. A1 Keyes, David E. YR 2022 SN 1530-2075 K1 Maximum likelihood estimation K1 Symmetric matrices K1 Runtime K1 Computational modeling K1 Linear algebra K1 Tail K1 Predictive models K1 Hierarchical Off Diagonal Low Rank approximation K1 Large scale geo statistical application K1 Likelihood approximation K1 Non Gaussian random field K1 Tile low rank approximation SP 379 OP 389 LK http://dx.doi.org/https://doi.org/10.1109/IPDPS53621.2022.00044 DO https://doi.org/10.1109/IPDPS53621.2022.00044 SF ELIB - SuUB Bremen
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