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10.21105.joss.01229.crossref.xml
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10.21105.joss.01229.crossref.xml
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<doi>10.1186/s12918-017-0484-3</doi>
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<unstructured_citation>Craig, Peter S. and Goldstein, Michael and Seheult, Allan H. and Smith, James A., Gatsonis, Constantine and Hodges, James S. and Kass, Robert E. and McCulloch, Robert and Rossi, Peter and Singpurwalla, Nozer D., Pressure Matching for Hydrocarbon Reservoirs: A Case Study in the Use of Bayes Linear Strategies for Large Computer Experiments, Case Studies in Bayesian Statistics, 1997, Springer New York, New York, NY, 37–93, 978-1-4612-2290-3</unstructured_citation>
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<unstructured_citation>Scikit-learn: Machine Learning in Python, Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E., Journal of Machine Learning Research, 12, 2825–2830, 2011</unstructured_citation>
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<unstructured_citation>Oliphant, Travis, NumPy: A guide to NumPy, 2006, USA: Trelgol Publishing, http://www.numpy.org/</unstructured_citation>
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