@book{rasmussen2006gaussian,
  title     = {{G}aussian processes for machine learning},
  author    = {Rasmussen, Carl Edward and Williams, Christopher K},
  number    = {3},
  year      = {2006},
  publisher = {MIT press Cambridge, MA},
  doi = {10.7551/mitpress/3206.001.0001}
}

@article{matthews2017gpflow,
  title   = {{GPflow}: A {Gaussian} Process Library using {TensorFlow}.},
  author  = {Matthews, Alexander G de G and Van Der Wilk, Mark and Nickson, Tom and Fujii, Keisuke and Boukouvalas, Alexis and Le{\'o}n-Villagr{\'a}, Pablo and Ghahramani, Zoubin and Hensman, James},
  journal = {Journal of Machine Learning Research},
  volume  = {18},
  number  = {40},
  pages   = {1--6},
  year    = {2017}
}

@inproceedings{abadi2016tensorflow,
  title     = {{TensorFlow}: A System for Large-Scale Machine Learning},
  author    = {Abadi, Mart{\'\i}n and Barham, Paul and Chen, Jianmin and Chen, Zhifeng and Davis, Andy and Dean, Jeffrey and Devin, Matthieu and Ghemawat, Sanjay and Irving, Geoffrey and Isard, Michael and others},
  booktitle = {12th {USENIX} symposium on operating systems design and implementation (OSDI 16)},
  pages     = {265--283},
  year      = {2016}
}


@article{gardner2018gpytorch,
  title   = {{G{P}y{T}orch}: Blackbox matrix-matrix {G}aussian process inference with {GPU} acceleration},
  author  = {Gardner, Jacob and Pleiss, Geoff and Weinberger, Kilian Q and Bindel, David and Wilson, Andrew G},
  journal = {Advances in neural information processing systems},
  volume  = {31},
  year    = {2018}
}


@software{deepmind2020jax,
  title  = {The {D}eep{M}ind {JAX} {E}cosystem},
  author = {Babuschkin, Igor and Baumli, Kate and Bell, Alison and Bhupatiraju, Surya and Bruce, Jake and Buchlovsky, Peter and Budden, David and Cai, Trevor and Clark, Aidan and Danihelka, Ivo and Fantacci, Claudio and Godwin, Jonathan and Jones, Chris and Hennigan, Tom and Hessel, Matteo and Kapturowski, Steven and Keck, Thomas and Kemaev, Iurii and King, Michael and Martens, Lena and Mikulik, Vladimir and Norman, Tamara and Quan, John and Papamakarios, George and Ring, Roman and Ruiz, Francisco and Sanchez, Alvaro and Schneider, Rosalia and Sezener, Eren and Spencer, Stephen and Srinivasan, Srivatsan and Stokowiec, Wojciech and Viola, Fabio},
  url    = {http://github.com/deepmind},
  year   = {2020}
}

@software{jax2018github,
  author  = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James Johnson and Chris Leary and Dougal Maclaurin and George Necula and Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao Zhang},
  title   = {{JAX}: composable transformations of {P}ython+{N}um{P}y programs},
  url     = {http://github.com/google/jax},
  version = {0.2.5},
  year    = {2018}
}

@article{phan2019composable,
  author  = {Phan, Du and Pradhan, Neeraj and Jankowiak, Martin},
  title   = {Composable Effects for Flexible and Accelerated Probabilistic Programming in {NumPyro}},
  journal = {arXiv preprint arXiv:1912.11554},
  year    = {2019},
  doi = {10.48550/arxiv.1912.11554}
}

@article{carpenter2017stan,
  title     = {Stan: A probabilistic programming language},
  author    = {Carpenter, Bob and Gelman, Andrew and Hoffman, Matthew D and Lee, Daniel and Goodrich, Ben and Betancourt, Michael and Brubaker, Marcus and Guo, Jiqiang and Li, Peter and Riddell, Allen},
  journal   = {Journal of Statistical Software},
  volume    = {76},
  number    = {1},
  year      = {2017},
  publisher = {Columbia Univ., New York, NY (United States); Harvard Univ., Cambridge, MA~…},
  doi = {10.18637/jss.v076.i01}
}


@article{Salvatier2016,
  doi       = {10.7717/peerj-cs.55},
  url       = {https://doi.org/10.7717/peerj-cs.55},
  year      = {2016},
  month     = {apr},
  publisher = {{PeerJ}},
  volume    = {2},
  pages     = {e55},
  author    = {John Salvatier and Thomas V. Wiecki and Christopher Fonnesbeck},
  title     = {Probabilistic programming in {P}ython using {PyMC}3},
  journal   = {{PeerJ} Computer Science}
}

@article{wilkinson2021bayesnewton,
  title   = {{B}ayes-{N}ewton Methods for Approximate {B}ayesian Inference with {PSD} Guarantees},
  author  = {Wilkinson, William J. and S\"arkk\"a, Simo and Solin, Arno},
  journal = {arXiv preprint arXiv:2111.01721},
  doi = {10.48550/arxiv.2111.01721},
  year    = {2021}
}

@article{fairbrother2022gaussianprocesses,
  title={GaussianProcesses.jl: A Nonparametric Bayes Package for the Julia Language},
  volume={102},
  url={https://www.jstatsoft.org/index.php/jss/article/view/v102i01},
  doi={10.18637/jss.v102.i01},
  abstract={Gaussian processes are a class of flexible nonparametric Bayesian tools that are widely used across the sciences, and in industry, to model complex data sources. Key to applying Gaussian process models is the availability of well-developed open source software, which is available in many programming languages. In this paper, we present a tutorial of the GaussianProcesses.jl package that has been developed for the Julia programming language. GaussianProcesses.jl utilizes the inherent computational benefits of the Julia language, including multiple dispatch and just-in-time compilation, to produce a fast, flexible and user-friendly Gaussian processes package. The package provides many mean and kernel functions with supporting inference tools to fit exact Gaussian process models, as well as a range of alternative likelihood functions to handle non-Gaussian data (e.g., binary classification models) and sparse approximations for scalable Gaussian processes. The package makes efficient use of existing Julia packages to provide users with a range of optimization and plotting tools.},
  number={1},
  journal={Journal of Statistical Software},
  author={Fairbrother, Jamie and Nemeth, Christopher and Rischard, Maxime and Brea, Johanni and Pinder, Thomas},
  year={2022},
  pages={1–36}
}

@article{vanhatalo2013gpstuff,
  title   = {{GPstuff}: {Bayesian} Modeling with {Gaussian} Processes},
  author  = {Vanhatalo, Juho and Riihimäki, Jaako and Hartikainen, Jouni and Jylänki, Pasi and Tolvanen, Ville and Vehtari, Aki},
  journal = {Journal of Machine Learning Research},
  year    = {2013}
}

@article{pinder2022street,
  title   = {Street-Level Air Pollution Modelling with Graph {Gaussian} Processes},
  author  = {Pinder, Thomas and Turnbull, Kathryn and Nemeth, Christopher and Leslie, David},
  journal = {ICLR: AI for Earth and Space Science},
  year    = {2022}
}

@article{pinder2021gaussian,
  title   = {{Gaussian} Processes on Hypergraphs},
  author  = {Pinder, Thomas and Turnbull, Kathryn and Nemeth, Christopher and Leslie, David},
  journal = {arXiv preprint arXiv:2106.01982},
  doi = {10.48550/arxiv.2106.01982},
  year    = {2021}
}

@misc{gpy2014,
  author    = {{GPy}},
  title     = {{GPy}: A {Gaussian} process framework in {P}ython},
  url       = {http://github.com/SheffieldML/GPy},
  year      = {2012},
  publisher = {Git{H}ub},
  journal   = {Git{H}ub repository}
}

@misc{blackjax2021,
  author    = {{BlackJax}},
  title     = {{BlackJAX}; a sampling library designed for ease of use, speed and modularity. },
  url       = {https://github.com/blackjax-devs/blackjax/},
  year      = {2021},
  publisher = {Git{H}ub},
  journal   = {Git{H}ub repository}
}

@misc{stheno2022tebbutt,
  title     = {{Stheno.jl.}},
  author    = {Tebbutt W, Bruinsma W, Turner RE},
  url       = {https://github.com/JuliaGaussianProcesses/Stheno.jl},
  year      = {2022},
  publisher = {Git{H}ub},
  journal   = {Git{H}ub repository}
}

@misc{stheno2022bruinsma,
  title     = {{Stheno}},
  author    = {Wessel Bruinsma},
  url       = {https://github.com/wesselb/stheno},
  year      = {2022},
  publisher = {Git{H}ub},
  journal   = {Git{H}ub repository}
}

@misc{dfm2021tinygp,
  title     = {{TinyGP}},
  author    = {Dan Foreman-Mackey},
  url       = {https://github.com/dfm/tinygp},
  year      = {2021},
  publisher = {Git{H}ub},
  journal   = {Git{H}ub repository}
}

@misc{objax2020github,
  author  = {{Objax Developers}},
  title   = {{Objax}},
  url     = {https://github.com/google/objax},
  version = {1.2.0},
  year    = {2020}
}

@article{hensman2013gaussian,
  author    = {James Hensman and
               Nicol{\'{o}} Fusi and
               Neil D. Lawrence},
  editor    = {Ann E. Nicholson and
               Padhraic Smyth},
  title     = {Gaussian Processes for Big Data},
  booktitle = {Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial
               Intelligence, {UAI} 2013, Bellevue, WA, USA, August 11-15, 2013},
  publisher = {{AUAI} Press},
  year      = {2013}
}



@article{rasmussen2010gaussian,
  title     = {{Gaussian} processes for machine learning ({GPML}) toolbox},
  author    = {Rasmussen, Carl Edward and Nickisch, Hannes},
  journal   = {The Journal of Machine Learning Research},
  volume    = {11},
  pages     = {3011--3015},
  year      = {2010},
  publisher = {JMLR. org}
}


@inproceedings{fajou20a,
  title     = {Automated Augmented Conjugate Inference for Non-conjugate {Gaussian} Process Models},
  author    = {Galy-Fajou, Theo and Wenzel, Florian and Opper, Manfred},
  booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics},
  pages     = {3025--3035},
  year      = {2020},
  editor    = {Silvia Chiappa and Roberto Calandra},
  volume    = {108},
  series    = {Proceedings of Machine Learning Research},
  month     = {26--28 Aug},
  publisher = {PMLR},
  pdf       = {http://proceedings.mlr.press/v108/galy-fajou20a/galy-fajou20a.pdf},
  url       = { http://proceedings.mlr.press/v108/galy-fajou20a.html }
}

@inproceedings{borovitskiy2021matern,
  title        = {Mat{\'e}rn {G}aussian processes on graphs},
  author       = {Borovitskiy, Viacheslav and Azangulov, Iskander and Terenin, Alexander and Mostowsky, Peter and Deisenroth, Marc and Durrande, Nicolas},
  booktitle    = {International Conference on Artificial Intelligence and Statistics},
  pages        = {2593--2601},
  year         = {2021},
  organization = {PMLR}
}

@article{mallasto2017learning,
  title   = {Learning from uncertain curves: The 2-{W}asserstein metric for {G}aussian processes},
  author  = {Mallasto, Anton and Feragen, Aasa},
  journal = {Advances in Neural Information Processing Systems},
  volume  = {30},
  year    = {2017}
}

@article{matheron1963principles,
  title     = {Principles of geostatistics},
  author    = {Matheron, Georges},
  journal   = {Economic geology},
  volume    = {58},
  number    = {8},
  pages     = {1246--1266},
  year      = {1963},
  publisher = {Society of Economic Geologists},
  doi = {10.2113/gsecongeo.58.8.1246}
}


@article{mockus1978application,
  title   = {The application of {B}ayesian methods for seeking the extremum},
  author  = {Mockus, Jonas and Tiesis, Vytautas and Zilinskas, Antanas},
  journal = {Towards global optimization},
  volume  = {2},
  number  = {117-129},
  pages   = {2},
  year    = {1978},
  doi = {10.1007/3-540-07165-2_55}
}

@inproceedings{deisenroth2011pilco,
  title        = {{PILCO}: A model-based and data-efficient approach to policy search},
  author       = {Deisenroth, Marc and Rasmussen, Carl E},
  booktitle    = {Proceedings of the 28th International Conference on machine learning (ICML-11)},
  pages        = {465--472},
  year         = {2011},
  organization = {Citeseer}
}

@inproceedings{amos2022ensembles,
  title        = {Probabilistic climate model projections, using {G}aussian Processes and Optimal Transport},
  author       = {Amos, Matthew and Pinder, Thomas and Nemeth, Christopher and Leslie, David and Young, Paul},
  booktitle    = {In preparation},
  year         = {2022},
}

@inproceedings{dodd2022ensembles,
  title        = {Bayesian adaptive sampling in a hostile deep-sea environment},
  author       = {Dodd, Daniel and Cripps, Edward and Jonathan, Phil and Leslie, David},
  booktitle    = {In preparation},
  year         = {2022},
}
