- Lavish intros
- Brutally quick intro
- Kernels
- Prior with a mean functions
- Using state filtering
- On lattice observations
- On manifolds
- By variational inference
- With inducing variables
- By variational inference with inducing variables
- With vector output
- Deep
- Neural processes
- Non-Gaussian prior that looks a bit similar
- Observation likelihoods
- Density estimation
- Approximation with dropout
- Inhomogeneous with covariates
- For dimension reduction
- Pathwise/Matheron updates
- Implementations
- References

Gaussian random processes/fields are stochastic processes/fields with jointly Gaussian distributions of observations.
While “Gaussian *process* regression” is not wrong *per se*, there is a common convention in stochastic process theory (and also in pedagogy) to use *process* to talk about some notionally time-indexed process and *field* to talk about ones that have a some space-like index without a special arrow of time.
This leads to much confusion, because Gaussian *field* regression is what we usually want to talk about
(although the arrow-of-time can pop up usefully).
Hereafter I use “field” and “process” interchangeably, as everyone does in this corner of the discipline.

In machine learning, Gaussian fields are used often as a means of regression or classification, since it is fairly easy to conditionalize a Gaussian field on data and produce a posterior distribution over functions. Because the reuslting regression function can have some very funky and weird posterior distributions, we can think of it as a kind of nonparametric Bayesian inference, although as always with that term we probably want to be careful with it; in fact GP regression typically has parameters.

I would further add that GPs are the crystal meth of machine learning methods, in terms of the addictiveness, and of the passion of the people who use it.

The central trick is using a clever union of Hilbert space tricks and probability to give a probabilistic interpretation of functional regression as a kind of nonparametric Bayesian inference.

Useful side divergence into
representer theorems and Karhunen-Loève expansions for give us a helpful interpretation.
Regression using Gaussian processes is common
e.g. spatial statistics
where it arises as *kriging*.
Cressie (1990) traces a history of this idea via Matheron (1963a), to works of Krige (1951).

## Lavish intros

I am not the right guy to provide the canonical introduction, because it already exists. Specifically, Rasmussen and Williams (2006). Moreover, because GP regression is so popular and so elegant, there are many excellent interactive introductions online.

This lecture by the late David Mackay is probably good; the man could talk.

There is also a well-illustrated and elementary introduction by Yuge Shi. There are many, many more.

Gaussianprocess.org is a classic.

A Visual Exploration of Gaussian Processes recommends the following:

- Interactive visualization of Gaussian processes by ST John that joins together the different concepts introduced throughout this article.
- Gaussian process regression demo by Tomi Peltola
- Gaussian Processes for Dummies by Katherine Bailey
- Intuition behind Gaussian Processes by Mike McCourt
- Fitting Gaussian Process Models in Python by Chris Fonnesbeck
- A Practical Guide to Gaussian Processes by Marc Peter Deisenroth, Yicheng Luo, and Mark van der Wilk: heuristics for initializing and optimizing Gaussian processes
If you want more of a hands-on experience, there are also many Python notebooks available:

- Fitting Gaussian Process Models in Python by Chris Fonnesbeck
- Gaussian process lecture by Andreas Damianou

Already read all those? Try the brutally quick intro.

## Brutally quick intro

J. T. Wilson et al. (2021) have a dense and useful perspective. If you are used to this field, they might reboot your perspective. If you are new to the GP area, see the more instructive intros.

A Gaussian process (GP) is a random function \(f: \mathcal{X} \rightarrow \mathbb{R}\), such that, for any finite collection of points \(\mathbf{X} \subset \mathcal{X}\), the random vector \(\boldsymbol{f}=f(\mathbf{X})\) follows a Gaussian distribution. Such a process is uniquely identified by a mean function \(\mu: \mathcal{X} \rightarrow \mathbb{R}\) and a positive semi-definite kernel \(k: \mathcal{X} \times \mathcal{X} \rightarrow \mathbb{R}\). Hence, if \(f \sim \mathcal{G} \mathcal{P}(\mu, k)\), then \(\boldsymbol{f} \sim \mathcal{N}(\boldsymbol{\mu}, \mathbf{K})\) is multivariate normal with mean \(\boldsymbol{\mu}=\mu(\mathbf{X})\) and covariance \(\mathbf{K}=k(\mathbf{X}, \mathbf{X})\).

[…] we investigate different ways of reasoning about the random variable \(\boldsymbol{f}_* \mid \boldsymbol{f}_n=\boldsymbol{y}\) for some non-trivial partition \(\boldsymbol{f}=\boldsymbol{f}_n \oplus \boldsymbol{f}_*\). Here, \(\boldsymbol{f}_n=f\left(\mathbf{X}_n\right)\) are process values at a set of training locations \(\mathbf{X}_n \subset \mathbf{X}\) where we would like to introduce a condition \(\boldsymbol{f}_n=\boldsymbol{y}\), while \(\boldsymbol{f}_*=f\left(\mathbf{X}_*\right)\) are process values at a set of test locations \(\mathbf{X}_* \subset \mathbf{X}\) where we would like to obtain a random variable \(\boldsymbol{f}_* \mid \boldsymbol{f}_n=\boldsymbol{y}\).

[…] we may obtain \(\boldsymbol{f}_* \mid \boldsymbol{y}\) by first finding its conditional distribution. Since process values \(\left(\boldsymbol{f}_n, \boldsymbol{f}_*\right)\) are defined as jointly Gaussian, this procedure closely resembles that of [the finite-dimensional case]: we factor out the marginal distribution of \(\boldsymbol{f}_n\) from the joint distribution \(p\left(\boldsymbol{f}_n, \boldsymbol{f}_*\right)\) and, upon canceling, identify the remaining distribution as \(p\left(\boldsymbol{f}_* \mid \boldsymbol{y}\right)\). Having done so, we find that the conditional distribution is the Gaussian \(\mathcal{N}\left(\boldsymbol{\mu}_{* \mid y}, \mathbf{K}_{*, * \mid y}\right)\) with moments \[\begin{aligned} \boldsymbol{\mu}_{* \mid \boldsymbol{y}}&=\boldsymbol{\mu}_*+\mathbf{K}_{*, n} \mathbf{K}_{n, n}^{-1}\left(\boldsymbol{y}-\boldsymbol{\mu}_n\right) \\ \mathbf{K}_{*, * \mid \boldsymbol{y}}&=\mathbf{K}_{*, *}-\mathbf{K}_{*, n} \mathbf{K}_{n, n}^{-1} \mathbf{K}_{n, *}\end{aligned} \]

## Kernels

a.k.a. covariance models.

GP regression models are kernel machines. As such covariance kernels are the parameters. More or less. One can also parameterise with a mean function, but (see next) let us ignore that detail for now because usually we do not use them.

## Prior with a mean functions

Almost immediate but not *quite* trivial
(Rasmussen and Williams 2006, 2.7).

TODO: discuss identifiability.

## Using state filtering

When one dimension of the input vector can be interpreted as a time dimension we are Kalman filtering Gaussian Processes, which has benefits in terms of speed and hipness.

## On lattice observations

## On manifolds

I would like to read Terenin on GPs on Manifolds who also makes a suggestive connection to SDEs, which is the filtering GPs trick again.

## By variational inference

🏗

## With inducing variables

“Sparse GP”. See Quiñonero-Candela and Rasmussen (2005). 🏗

## By variational inference with inducing variables

See GP factoring.

## With vector output

## Neural processes

See neural processes.

## Non-Gaussian prior that looks a bit similar

## Observation likelihoods

Gaussian processes need not have a Gaussian likelihood. Classification etc. TBD

## Density estimation

Can I infer a density using GPs? Yes. One popular method is apparently the logistic Gaussian process (Tokdar 2007; Lenk 2003).

## Approximation with dropout

Unconvincing in practice. See NN ensembles for some vague notes.

## Inhomogeneous with covariates

Integrated nested Laplace approximation connects to GP-as-SDE idea, I think?

## For dimension reduction

e.g. GP-LVM (N. Lawrence 2005). 🏗

## Pathwise/Matheron updates

See pathwise GP.

## Implementations

## References

*Canadian Journal of Statistics*26 (1): 127–37.

*Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence*, 2–9. UAI ’04. Arlington, Virginia, United States: AUAI Press.

*arXiv:1705.07104 [Cs, Stat]*, November.

*arXiv:1403.6015 [Astro-Ph, Stat]*, April.

*IEEE Transactions on Information Theory*64 (10): 6620–37.

*arXiv:1805.00753 [Stat]*, April.

*Probability Theory and Related Fields*138 (1-2): 33–73.

*Proceedings of the 20th International Conference on Neural Information Processing Systems*, 153–60. NIPS’07. USA: Curran Associates Inc.

*Journal of Machine Learning Research*20 (117): 1–63.

*arXiv:2006.10160 [Cs, Stat]*, June.

*Journal of Machine Learning Research*21 (131): 1–63.

*2016 International Joint Conference on Neural Networks (IJCNN)*, 3338–45. Vancouver, BC, Canada: IEEE.

*Mathematical Geology*22 (3): 239–52.

*Statistics for Spatial Data*. John Wiley & Sons.

*Statistics for Spatio-Temporal Data*. Wiley Series in Probability and Statistics 2.0. John Wiley and Sons.

*Neural Computation*14 (3): 641–68.

*Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic*, 657–63. NIPS’01. Cambridge, MA, USA: MIT Press.

*Proceedings of the 25th International Conference on Machine Learning*, 192–99. ICML ’08. New York, NY, USA: ACM Press.

*PMLR*.

*Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy*, edited by Wei Lee Woon, Zeyar Aung, Oliver Kramer, and Stuart Madnick, 94–106. Lecture Notes in Computer Science. Cham: Springer International Publishing.

*arXiv:1903.03986 [Cs, Stat]*, March.

*Artificial Intelligence and Statistics*, 207–15.

*Advances in Neural Information Processing Systems 24*, edited by J. Shawe-Taylor, R. S. Zemel, P. L. Bartlett, F. Pereira, and K. Q. Weinberger, 2510–18. Curran Associates, Inc.

*Advances in Neural Information Processing Systems 28*, 1414–22. NIPS’15. Cambridge, MA, USA: MIT Press.

*arXiv:2012.00152 [Cs, Stat]*, November.

*Handbook of Mathematical Geosciences: Fifty Years of IAMG*, edited by B.S. Daya Sagar, Qiuming Cheng, and Frits Agterberg, 3–24. Cham: Springer International Publishing.

*Journal of Machine Learning Research*19 (1): 2100–2145.

*arXiv:2105.04504 [Cs, Stat]*.

*Proceedings of the 30th International Conference on Machine Learning (ICML-13)*, 1166–74.

*arXiv:1505.02965 [Math, Stat]*, May.

*Advances in Neural Information Processing Systems 30*, edited by I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, 5309–19. Curran Associates, Inc.

*Mathematical Geology*39 (6): 607–23.

*Journal of Machine Learning Research*6 (Apr): 615–37.

*The Annals of Statistics*1 (2): 209–30.

*arXiv:2010.10876 [Cs]*, October.

*arXiv:1711.00799 [Stat]*, November.

*Advances in Neural Information Processing Systems 27*, edited by Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, 3680–88. Curran Associates, Inc.

*Advances in Neural Information Processing Systems 26*, edited by C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, 3156–64. Curran Associates, Inc.

*Proceedings of the 33rd International Conference on Machine Learning (ICML-16)*.

*arXiv:1402.1412 [Stat]*, February.

*Proceedings of the 20th International Conference on Artificial Intelligence and Statistics*, 353–61. PMLR.

*Proceedings of the 32nd International Conference on Neural Information Processing Systems*, 31:7587–97. NIPS’18. Red Hook, NY, USA: Curran Associates Inc.

*arXiv:1802.08903 [Cs, Stat]*, February.

*arXiv:1807.01613 [Cs, Stat]*, July, 10.

*Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences*371 (1984): 20110553.

*IEEE Transactions on Pattern Analysis and Machine Intelligence*37 (2): 424–36.

*Proceedings of the 22nd International Conference on Machine Learning - ICML ’05*, 241–48. Bonn, Germany: ACM Press.

*Journal of Statistical Software*72 (1).

*Journal of Computational and Graphical Statistics*24 (2): 561–78.

*Handbook of Uncertainty Quantification*, edited by Roger Ghanem, David Higdon, and Houman Owhadi, 1–37. Cham: Springer International Publishing.

*Proceedings of the Conference on Uncertainty in Artificial Intelligence*.

*2010 IEEE International Workshop on Machine Learning for Signal Processing*, 379–84. Kittila, Finland: IEEE.

*Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence*, 282–90. UAI’13. Arlington, Virginia, USA: AUAI Press.

*Pattern Recognition Letters*45 (August): 85–91.

*arXiv:1806.10234 [Cs, Stat]*, June.

*Conference on Uncertainty in Artificial Intelligence*, 789–98. PMLR.

*Learning in Graphical Models*. Cambridge, Mass.: MIT Press.

*2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)*, 1–6. Vietri sul Mare, Salerno, Italy: IEEE.

*arXiv:2001.08055 [Physics, Stat]*, January.

*ICLR 2014 Conference*.

*Autonomous Robots*, 27:75–90.

*Mathematical and Computer Modelling of Dynamical Systems*11 (4): 411–24.

*UAI17*.

*Journal of the Southern African Institute of Mining and Metallurgy*52 (6): 119–39.

*arXiv:1308.0399 [Stat]*, August.

*Journal of Machine Learning Research*6 (Nov): 1783–1816.

*Proceedings of the 26th Annual International Conference on Machine Learning*, 601–8. ICML ’09. New York, NY, USA: ACM.

*Proceedings of the 16th Annual Conference on Neural Information Processing Systems*, 609–16.

*Journal of Machine Learning Research*11 (Jun): 1865–81.

*ICLR*.

*Journal of Computational and Graphical Statistics*12 (3): 548–65.

*Journal of the Royal Statistical Society: Series B (Statistical Methodology)*73 (4): 423–98.

*IEEE Transactions on Signal Processing*59 (7): 3155–67.

*Twenty-Eighth AAAI Conference on Artificial Intelligence*.

*Advances in Neural Information Processing Systems*. Vol. 32. Curran Associates, Inc.

*NATO ASI Series. Series F: Computer and System Sciences*168: 133–65.

*Information Theory, Inference & Learning Algorithms*, Chapter 45. Cambridge University Press.

*Traité de Géostatistique Appliquée. 2. Le Krigeage*. Editions Technip.

*Economic Geology*58 (8): 1246–66.

*arXiv:1610.08733 [Stat]*, October.

*Journal of Process Control*, DYCOPS-CAB 2016, 60 (December): 82–94.

*Proceedings of ICLR*.

*Journal of Machine Learning Research*6 (Jul): 1099–1125.

*Neural Computation*17 (1): 177–204.

*SIAM/ASA Journal on Uncertainty Quantification*, February, 96–124.

*arXiv:2104.14987 [Stat]*, April.

*arXiv:1911.00002 [Cs, Stat]*, October.

*SSRN Electronic Journal*.

*International Conference on Machine Learning*, 3789–98.

*Journal of the Royal Statistical Society: Series B (Methodological)*40 (1): 1–24.

*Biometrika*99 (3): 511–31.

*Journal of Open Source Software*7 (75): 4455.

*Advances in Neural Information Processing Systems*33.

*Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence*, 450–57. UAI’10. Arlington, Virginia, USA: AUAI Press.

*Journal of Machine Learning Research*6 (Dec): 1939–59.

*arXiv:1701.02440 [Cs, Math, Stat]*, January.

*Gaussian Processes for Machine Learning*. Adaptive Computation and Machine Learning. Cambridge, Mass: MIT Press.

*2010 13th International Conference on Information Fusion*, 1–9.

*arXiv:2105.14594 [Cs, Stat]*, May.

*arXiv:2004.11408 [Stat]*, April.

*Proceedings of The 24th International Conference on Artificial Intelligence and Statistics*, 1837–45. PMLR.

*Proceedings of the 27th International Conference on International Conference on Machine Learning*, 927–34. ICML’10. Madison, WI, USA: Omnipress.

*arXiv:1910.09349 [Cs, Stat]*, March.

*Advances In Neural Information Processing Systems*.

*International Conference on Artificial Intelligence and Statistics*, 689–97.

*Artificial Neural Networks and Machine Learning – ICANN 2011*, edited by Timo Honkela, Włodzisław Duch, Mark Girolami, and Samuel Kaski, 6792:151–58. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer.

*Bayesian Filtering and Smoothing*. Institute of Mathematical Statistics Textbooks 3. Cambridge, U.K. ; New York: Cambridge University Press.

*Artificial Intelligence and Statistics*.

*IEEE Signal Processing Magazine*30 (4): 51–61.

*Proceedings of the 31st International Conference on Neural Information Processing Systems*, 1696–706. NIPS’17. Red Hook, NY, USA: Curran Associates Inc.

*Artificial Intelligence and Statistics*, 877–85. PMLR.

*Scalable Bayesian Spatial Analysis with Gaussian Markov Random Fields*. Vol. 15. Linköping Studies in Statistics. Linköping: Linköping University Electronic Press.

*arXiv:1809.02010 [Cs, Stat]*, September.

*Advances in Neural Information Processing Systems*, 1257–64.

*Statistics and Computing*30 (2): 419–46.

*arXiv:2006.15641 [Cs, Stat]*, June.

*arXiv:1908.05726 [Math, Stat]*, August.

*International Conference on Artificial Intelligence and Statistics*, 567–74. PMLR.

*Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics*, 844–51.

*Journal of Computational and Graphical Statistics*16 (3): 633–55.

*IEEE Transactions on Signal Processing*62 (23): 6171–83.

*Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics*, 868–75.

*Journal of Machine Learning Research*14 (April): 1175−1179.

*arXiv:1206.5754 [Cs, Stat]*, July.

*Proceedings of the 25th International Conference on Machine Learning*, 1112–19. ICML ’08. New York, NY, USA: ACM.

*Computer Graphics Forum*25 (3): 635–44.

*Advances in Neural Information Processing Systems*, 32:14648–59. Red Hook, NY, USA.

*Spatio-Temporal Statistics with R*.

*NIPS 2014 Workshop on Advances in Variational Inference*.

*arXiv:1901.11436 [Cs, Eess, Stat]*, January.

*Advances in Neural Information Processing Systems*, 682–88.

*Advances in Neural Information Processing Systems 21*, edited by D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, 265–72. Curran Associates, Inc.

*International Conference on Machine Learning*.

*arXiv:1510.07389 [Cs, Stat]*, October.

*Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence*, 736–44. UAI’11. Arlington, Virginia, United States: AUAI Press.

*Machine Learning and Knowledge Discovery in Databases*, edited by Peter A. Flach, Tijl De Bie, and Nello Cristianini, 858–61. Lecture Notes in Computer Science. Springer Berlin Heidelberg.

*Proceedings of the 29th International Coference on International Conference on Machine Learning*, 1139–46. ICML’12. Madison, WI, USA: Omnipress.

*Proceedings of the 32Nd International Conference on International Conference on Machine Learning - Volume 37*, 1775–84. ICML’15. Lille, France: JMLR.org.

*Proceedings of the 37th International Conference on Machine Learning*, 10292–302. PMLR.

*Journal of Machine Learning Research*22 (105): 1–47.

*Proceedings of NeurIPS 2020*.

## No comments yet. Why not leave one?