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Line 679: <ref name="vanhatalo2013">{{cite journal \|last1=Vanhatalo \|first1=Jarno \|last2=Riihimäki \|first2=Jaakko \|last3=Hartikainen \|first3=Jouni \|last4=Jylänki \|first4=Pasi \|last5=Tolvanen \|first5=Ville \|last6=Vehtari \|first6=Aki \|title=GPstuff: Bayesian Modeling with Gaussian Processes \|journal=Journal of Machine Learning Research \|date=Apr 2013 \|volume=14 \|pages=1175−1179 \|url=http://jmlr.csail.mit.edu/papers/v14/vanhatalo13a.html \|accessdate=23 May 2020}}</ref> <ref name="marelli2014">{{cite journal \|last1=Marelli \|first1=Stefano \|last2=Sudret \|first2=Bruno \|title=UQLab: a framework for uncertainty quantification in MATLAB \|journal=Vulnerability, Uncertainty, and Risk. Quantification, Mitigation, and Management \|date=2014 \|pages=2554–2563 \|doi=10.3929/ethz-a-010238238 \|isbn=978-0-7844-1360-9 \|url=https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/379365/eth-14488-01.pdf?sequence=1&isAllowed=y \|accessdate=28 May 2020}}</ref> <ref name="matthews2017">{{cite journal \|last1=Matthews \|first1=Alexander G. de G. \|last2=van der Wilk \|first2=Mark \|last3=Nickson \|first3=Tom \|last4=Fujii \|first4=Keisuke \|last5=Boukouvalas \|first5=Alexis \|last6=León-Villagrá \|first6=Pablo \|last7=Ghahramani \|first7=Zoubin \|last8=Hensman \|first8=James \|title=GPflow: A Gaussian process library using TensorFlow \|journal=Journal of Machine Learning Research \|date=April 2017 \|volume=18 \|issue=40 \|pages=1–6 \|arxiv=1610.08733 \|url=http://jmlr.org/papers/v18/16-537.html \|accessdate=6 July 2020}}</ref> Line 695: <ref name="sarkka2013">{{cite journal \|last1=Sarkka \|first1=Simo \|last2=Solin \|first2=Arno \|last3=Hartikainen \|first3=Jouni \|title=Spatiotemporal Learning via Infinite-Dimensional Bayesian Filtering and Smoothing: A Look at Gaussian Process Regression Through Kalman Filtering \|journal=IEEE Signal Processing Magazine \|date=2013 \|volume=30 \|issue=4 \|pages=51–61 \|doi=10.1109/MSP.2013.2246292 \|s2cid=7485363 \|url=https://ieeexplore.ieee.org/document/6530736 \|access-date=2 September 2021}}</ref> <ref name="bouhlel2019">{{cite journal \|last1=Saves\|first1=Paul \|last2=Lafage\|first2=Rémi \|last3=Bartoli \|first3=Nathalie \|last4=Diouane \|first4= Youssef \|last5=Bussemaker \|first5= Jasper \|last6=Lefebvre \|first6= Thierry \|last7=Hwang \|first7= John T. \|last8= Morlier \|first8= Joseph \|last9= Martins \|first9= Joaquim R.R.A. \|title=SMT 2.0: A Surrogate Modeling Toolbox with a focus on hierarchical and mixed variables Gaussian processes \|journal=Advances in Engineering Software \|date=2024 \|volume=188\|issue=1 \|pages=103571 \|doi=10.1016/j.advengsoft.2023.103571 \|url=https://www.sciencedirect.com/science/article/pii/S096599782300162X~~?via%3Dihub~~\|arxiv=2305.13998 }}</ref> <ref name="saves2024">{{cite journal \|last1=Bouhlel\|first1=Mohamed A. \|last2=Hwang \|first2= John T. \|last3=Bartoli \|first3=Nathalie \|last4=Lafage\|first4=Rémi \|last5= Morlier \|first5= Joseph \|last6= Martins \|first6= Joaquim R.R.A. \|title=A Python surrogate modeling framework with derivatives \|journal=Advances in Engineering Software \|date=2019\|volume=135\|issue=1 \|pages=102662\|doi=10.1016/j.advengsoft.2019.03.005 \|url=https://www.sciencedirect.com/science/article/pii/S0965997818309360~~?via%3Dihub~~}}</ref> <ref name="Porrello24">{{cite ~~journal~~book \|last1=Porrello \|first1=Christian \|last2=Dubreuil \|first2=Sylvain \|last3=Farhat \|first3=Charbel \|title=AIAA Aviation Forum and Ascend 2024 \|chapter=Bayesian Framework With Projection-Based Model Order Reduction for Efficient Global Optimization ~~\|journal=AIAA AVIATION FORUM AND ASCEND 2024~~ \|date=2024 \|pages=4580 \|doi=10.2514/6.2024-4580 \|isbn=978-1-62410-716-0 \|chapter-url=https://arc.aiaa.org/doi/abs/10.2514/6.2024-4580}}</ref> <ref name="Lafage2022">{{cite journal \|last1=Lafage \|first1=Rémi \|title=egobox, a Rust toolbox for efficient global optimization \|journal=Journal of Open Source Software \|date=2022 \|volume=7 \|issue=78 \|pages=4737 \|doi=10.21105/joss.04737 \|bibcode=2022JOSS....7.4737L \|url=https://joss.theoj.org/papers/10.21105/joss.04737.pdf}}</ref>

Comparison of Gaussian process software: Difference between revisions