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<ref name="foreman2017">{{cite journal |last1=Foreman-Mackey |first1=Daniel |last2=Angus |first2=Ruth |last3=Agol |first3=Eric |last4=Ambikasaran |first4=Sivaram |s2cid=88521913 |title=Fast and Scalable Gaussian Process Modeling with Applications to Astronomical Time Series |journal=The Astronomical Journal |date=9 November 2017 |volume=154 |issue=6 |page=220 |doi=10.3847/1538-3881/aa9332|arxiv=1703.09710 |bibcode=2017AJ....154..220F |doi-access=free }}</ref>
<ref name="gilboa2015">{{cite journal |last1=P. Cunningham |first1=John |last2=Gilboa |first2=Elad |last3=Saatçi |first3=Yunus |s2cid=6878550 |title=Scaling Multidimensional Inference for Structured Gaussian Processes |journal=IEEE Transactions on Pattern Analysis and Machine Intelligence |date=Feb 2015 |volume=37 |issue=2 |pages=424–436 |doi=10.1109/TPAMI.2013.192|pmid=26353252 |arxiv=1209.4120 |bibcode=2015ITPAM..37..424G }}</ref>
<ref name="zhang2005">{{cite book |last1=Leith |first1=D. J. |last2=Zhang |first2=Yunong |last3=Leithead |first3=W. E. |title=Proceedings of the 44th IEEE Conference on Decision and Control |chapter=Time-series Gaussian Process Regression Based on Toeplitz Computation of O(N²) Operations and O(N)-level Storage |s2cid=13627455 |date=2005 |pages=3711–3716 |doi=10.1109/CDC.2005.1582739|isbn=0-7803-9567-0 }}</ref>
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<ref name="couckuyt2014">{{cite journal |last1=Couckuyt |first1=Ivo |last2=Dhaene |first2=Tom |last3=Demeester |first3=Piet |title=ooDACE toolbox: a flexible object-oriented Kriging implementation |journal=Journal of Machine Learning Research |date=2014 |volume=15 |pages=3183–3186 |url=http://www.jmlr.org/papers/volume15/couckuyt14a/couckuyt14a.pdf |accessdate=8 July 2020}}</ref>
<ref name="zilber2021">{{cite journal |last1=Zilber |first1=Daniel |last2=Katzfuss |first2=Matthias |title=Vecchia–Laplace approximations of generalized Gaussian processes for big non-Gaussian spatial data |journal=Computational Statistics & Data Analysis |date=January 2021 |volume=153 |
<ref name="kalaitzis2011">{{cite journal |last1=Kalaitzis |first1=Alfredo |last2=Lawrence |first2=Neil D. |title=A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression |journal=BMC Bioinformatics |date=May 20, 2011 |volume=12 |issue=1 |pages=180 |doi=10.1186/1471-2105-12-180 |pmid=21599902 |pmc=3116489 |issn=1471-2105 |doi-access=free }}</ref>
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<ref name="baudin2015">{{cite book |first1=Michaël |last1=Baudin |first2=Anne |last2=Dutfoy |first3=Bertrand |last3=Iooss |first4=Anne-Laure |last4=Popelin |title=Handbook of Uncertainty Quantification |chapter=OpenTURNS: An Industrial Software for Uncertainty Quantification in Simulation |date=2015 |pages=1–38 |editor1= Roger Ghanem|editor2= David Higdon|editor3= Houman Owhadi|doi=10.1007/978-3-319-11259-6_64-1 |arxiv=1501.05242|isbn=978-3-319-11259-6 |s2cid=88513894 }}</ref>
<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
<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|arxiv=2305.13998 }}</ref>
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