Open Access
Issue |
EPJ Web Conf.
Volume 281, 2023
5th International Workshop on Nuclear Data Covariances (CW2022)
|
|
---|---|---|
Article Number | 00019 | |
Number of page(s) | 6 | |
DOI | https://doi.org/10.1051/epjconf/202328100019 | |
Published online | 29 March 2023 |
- T. Kariya, H. Kurata, Generalized Least Squares, 1st edn. (Wiley, 2007) [Google Scholar]
- D.W. Muir, A. Trkov, I. Kodeli et al., The Global Assessment of Nuclear Data, GANDR (EDP Sciences, 2007) [Google Scholar]
- W.P. Poenitz, S.E. Aumeier, Tech. Rep. ANL/NDM139, Argonne National Laboratory, Argonne, Illinois (1997) [Google Scholar]
- N.M. Larson, Tech. Rep. ORNL/TM-9179/R4, Oak Ridge National Laboratory, Oak Ridge (1998) [Google Scholar]
- D. Neudecker, R. Capote, H. Leeb, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 723, 163 (2013) [CrossRef] [Google Scholar]
- P. Helgesson, H. Sjöstrand, Annals of Nuclear Energy 120, 35 (2018) [CrossRef] [Google Scholar]
- J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, 1st edn. (Morgan Kaufmann, 2014) [Google Scholar]
- G. Schnabel, R. Capote, A. Koning et al., Nuclear data evaluation with Bayesian networks (2021), arxiv:2110.10322 [Google Scholar]
- G. Schnabel, IAEA-NDS/nucdataBaynet, https://github.com/IAEA-NDS/nucdataBaynet (2021) [Google Scholar]
- M. Dowle, A. Srinivasan, Data.table: Extension of ‘data.frame‘, https://CRAN.Rproject.org/package=data.table (2022) [Google Scholar]
- Wes McKinney, Data Structures for Statistical Computing in Python, in Proceedings of the 9th Python in Science Conference, edited by S. van der Walt, Jarrod Millman (2010), pp. 56–61 [Google Scholar]
- D. Bates, M. Maechler, Matrix: Sparse and dense matrix classes and methods, https://CRAN.Rproject.org/package=Matrix (2021) [Google Scholar]
- D.S. Sivia, Data Analysis: A Bayesian Tutorial (Clarendon Press, 1996), ISBN 978-0-19-851889-1 [Google Scholar]
- K. Levenberg, Quarterly of Applied Mathematics 2, 164 (1944) [Google Scholar]
- D.W. Marquardt, Journal of the Society for Industrial and Applied Mathematics 11, 431 (1963) [Google Scholar]
- P. Helgesson, H. Sjöstrand, Review of Scientific Instruments 88, 115114 (2017) [Google Scholar]
- C.E. Rasmussen, C.K.I. Williams, Gaussian Processes for Machine Learning (MIT Press, Cambridge, Mass., 2006), ISBN 0-262-18253-X 978-0-262-18253-9 [Google Scholar]
- V. Zerkin, B. Pritychenko, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 888, 31 (2018) [CrossRef] [Google Scholar]
- N. Otuka, E. Dupont, V. Semkova et al., Nuclear Data Sheets 120, 272 (2014) [Google Scholar]
- A. Carlson, V. Pronyaev, R. Capote et al., Nuclear Data Sheets 148, 143 (2018) [Google Scholar]
- G. Schnabel, GitHub IAEA-NDS/gmapy: Gmapy: A Python package for nuclear data evaluation, https://github.com/IAEA-NDS/gmapy [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.