Comparison of Gaussian process software: Difference between revisions

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! [https://celerite2.readthedocs.io/en/latest/ celerite2]

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! [https://smt.readthedocs.io/en/latest/ SMT]<ref name="saves2024" /><ref name="bouhlel2019" />

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| [[Python (programming language)|Python]]

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<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="saves2024bouhlel2019">{{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}}</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>

}}