---
title: "Gaussian process regression: formula and example"
description: "Gaussian process regression explained: variables, a worked example, related methods and assumptions. Bayesian non-parametric regression that gives predictions with uncertainty; also the surrogate in Bayesian optimisation."
url: https://scholar4ai.com/equations/gaussian-process-regression
markdown_url: https://scholar4ai.com/equations/gaussian-process-regression.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "Gaussian process regression explained: variables, a worked example, related methods and assumptions. Bayesian non-parametric regression that gives predictions with uncertainty; also the surrogate in Bayesian optimisation. (as of 2026-11-05)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-05T06:02:37+09:00
window: 2026-10-07 to 2026-11-05
source: OpenAlex (CC0)
---

# Gaussian process regression

$$
f \sim \mathcal{GP}\big(m(x),\ k(x, x')\big),\quad k(x, x') = \sigma^2 \exp\!\left(-\frac{\lVert x - x' \rVert^2}{2\ell^2}\right)
$$

Bayesian non-parametric regression that gives predictions with uncertainty; also the surrogate in Bayesian optimisation.

- area: Statistics and Bayesian methods
- 1 paper in the last 7 days
- Names searched for: Gaussian process, kriging

Back to the ranking: https://scholar4ai.com/rankings/equations.md

## Variables and notation

f is a random function with mean m and covariance kernel k; sigma² is signal variance, ell length scale and x,x' inputs; GP denotes a Gaussian process.

## Worked example

For sigma²=1,ell=2 and inputs one unit apart, covariance=exp(−1/8)≈0.882497. At identical inputs it equals 1.

## Related methods and when to use them

GP regression gives a distribution over functions; ordinary linear regression restricts the function form. This equation specifies a prior/kernel, not the full posterior; observation noise and training-data conditioning are also needed.

## Sources and further reading

- [Gaussian Processes for Machine Learning](https://doi.org/10.7551/mitpress/3206.001.0001)

Examples use illustrative inputs, not observed research results. Logarithms are natural unless stated otherwise. Guide updated 2026-11-05. Prepared by Scholar4AI. [Editorial and verification policy](/editorial-policy).

## Key findings

- Gaussian process regression was mentioned in 27 papers in the last 30 days (2026-10-07 to 2026-11-05), ranking #56 of the 248 equations and models mentioned.
- By journal category: Statistics (15), AI (6), Operations Research (3), Finance (2) and Economics (1).
- Most of them appeared in Journal of Multivariate Analysis (4), Computational Statistics & Data Analysis (2) and Spatial Statistics (2).
- Often mentioned together with Markov chain Monte Carlo (Metropolis-Hastings) (6), Random forest (4) and LASSO (3).
- Datasets named in the same papers: American Community Survey (2), World Development Indicators (World Bank) (1) and German Credit (UCI Statlog) (1).
- From Nov 2023 to Oct 2026, 1,340 archived papers mentioned Gaussian process regression in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Jul 2025 (54).
- From 2017 to 2026 (so far), Gaussian process regression was mentioned in 2,117 papers in the tracked journals, most of them in AI (959), statistics (626) and operations research (333).
- 423 of them were published in 2025, against 70 in 2017.
- Since 2023, the institutions with the most of these papers were Centre National de la Recherche Scientifique (37), Tsinghua University (25) and Chinese Academy of Sciences (20).
- The journals publishing most of them were Statistics and Computing (82), Spatial Statistics (65) and Artificial Intelligence Review (63).
- The most-cited of them is “A Comprehensive Survey of Continual Learning: Theory, Method and Application” (IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024), with 1,114 citations.

## Mentions over 36 months

*Papers mentioning it by publication month, Nov 2023 to Oct 2026 (titles, abstracts and keywords; the full texts of earlier months are still being searched)*

| Trend | Nov 23–Oct 24 | Nov 24–Oct 25 | Nov 25–Oct 26 | 36 months |
|---|---:|---:|---:|---:|
|  | 461 | 437 | 442 | 1,340 |

## Papers per year

*Papers per publication year and category*

| Category | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 (so far) |
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| [AI](https://scholar4ai.com/categories/ai.md) | 29 | 43 | 30 | 49 | 59 | 81 | 101 | 249 | 180 | 138 |
| [Statistics](https://scholar4ai.com/categories/statistics.md) | 28 | 44 | 33 | 41 | 37 | 43 | 61 | 94 | 116 | 129 |
| [Operations Research](https://scholar4ai.com/categories/operations-research.md) | 8 | 14 | 15 | 13 | 19 | 22 | 35 | 71 | 75 | 61 |
| [Economics](https://scholar4ai.com/categories/economics.md) | 4 | 3 | 1 | 3 | 1 | 6 | 8 | 23 | 26 | 33 |
| [Finance](https://scholar4ai.com/categories/finance.md) | 1 | 2 | 2 | 1 | 0 | 5 | 1 | 15 | 12 | 10 |
| [Information Systems](https://scholar4ai.com/categories/information-systems.md) | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 2 | 5 | 2 |
| [Management](https://scholar4ai.com/categories/management.md) | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 4 | 3 | 3 |
| [Health Economics and Policy](https://scholar4ai.com/categories/health-economics-and-policy.md) | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 3 | 1 |
| [Higher Education and Research](https://scholar4ai.com/categories/higher-education-and-research.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 2 |
| [Marketing](https://scholar4ai.com/categories/marketing.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 |
| [Tourism and Hospitality](https://scholar4ai.com/categories/tourism-and-hospitality.md) | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 |
| All categories | 70 | 108 | 82 | 108 | 116 | 157 | 208 | 465 | 423 | 380 |

## Where the papers come from

*Journals with the most papers, since 2023*

| # | Journal | Papers |
|---:|---|---:|
| 1 | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | 82 |
| 2 | [Spatial Statistics](https://scholar4ai.com/journals/spatial-statistics.md) | 65 |
| 3 | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 63 |
| 4 | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 62 |
| 5 | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 60 |
| 6 | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 53 |
| 7 | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 46 |
| 8 | [Transportation Research Part C Emerging Technologies](https://scholar4ai.com/journals/transportation-research-part-c-emerging-technologies.md) | 41 |
| 9 | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 40 |
| 10 | [IEEE Transactions on Neural Networks and Learning Systems](https://scholar4ai.com/journals/ieee-transactions-on-neural-networks-and-learning-systems.md) | 32 |

*Institutions with the most papers, since 2023*

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Centre National de la Recherche Scientifique](https://scholar4ai.com/institutions/centre-national-de-la-recherche-scientifique.md) | 37 |
| 2 | [Tsinghua University](https://scholar4ai.com/institutions/tsinghua-university.md) | 25 |
| 3 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 20 |
| 4 | [Hong Kong Polytechnic University](https://scholar4ai.com/institutions/hong-kong-polytechnic-university.md) | 18 |
| 5 | [Tongji University](https://scholar4ai.com/institutions/tongji-university.md) | 18 |
| 6 | [Central South University](https://scholar4ai.com/institutions/central-south-university.md) | 17 |
| 7 | [National University of Singapore](https://scholar4ai.com/institutions/national-university-of-singapore.md) | 17 |
| 8 | [King Abdullah University of Science and Technology](https://scholar4ai.com/institutions/king-abdullah-university-of-science-and-technology.md) | 15 |
| 9 | [North Carolina State University](https://scholar4ai.com/institutions/north-carolina-state-university.md) | 15 |
| 10 | [City University of Hong Kong](https://scholar4ai.com/institutions/city-university-of-hong-kong.md) | 14 |

*Countries with the most papers, since 2023*

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 389 |
| 2 | United States | 298 |
| 3 | United Kingdom | 180 |
| 4 | Germany | 119 |
| 5 | France | 100 |
| 6 | Italy | 96 |
| 7 | Spain | 77 |
| 8 | Australia | 75 |
| 9 | Canada | 73 |
| 10 | Japan | 50 |

*Keywords with the most papers, since 2023*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | Gaussian processes | 124 |
| 2 | Gaussian process regression | 107 |
| 3 | [uncertainty quantification](https://scholar4ai.com/keywords/uncertainty-quantification.md) | 78 |
| 4 | Bayesian optimization | 56 |
| 5 | surrogate models | 47 |
| 6 | [computational efficiency](https://scholar4ai.com/keywords/computational-efficiency.md) | 40 |
| 7 | [hyperparameter optimization](https://scholar4ai.com/keywords/hyperparameter-optimization.md) | 40 |
| 8 | Kriging surrogate model | 33 |
| 9 | kriging | 32 |
| 10 | [multi-objective optimization](https://scholar4ai.com/keywords/multi-objective-optimization.md) | 32 |

## Most-cited papers

*Most-cited papers since 2017*

| # | Citations | Per month | Title | Journal | Published | Authors |
|---:|---:|---:|---|---|---|---|
| 1 | 1,114 | 34.5 | [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://doi.org/10.1109/tpami.2024.3367329) | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 2024-02-26 | Liyuan Wang, Xingxing Zhang, Hang Su et al. |
| 2 | 1,065 | 40.2 | [Medical Image Segmentation Review: The Success of U-Net](https://doi.org/10.1109/tpami.2024.3435571) | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 2024-08-21 | Reza Azad, Ehsan Khodapanah Aghdam, Amelie Rauland et al. |
| 3 | 890 | 10.9 | [When Gaussian Process Meets Big Data: A Review of Scalable GPs](https://doi.org/10.1109/tnnls.2019.2957109) | [IEEE Transactions on Neural Networks and Learning Systems](https://scholar4ai.com/journals/ieee-transactions-on-neural-networks-and-learning-systems.md) | 2020-01-07 | Haitao Liu, Yew-Soon Ong, Xiaobo Shen et al. |
| 4 | 613 | 18.5 | [Autoencoders and their applications in machine learning: a survey](https://doi.org/10.1007/s10462-023-10662-6) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 2024-02-03 | Kamal Berahmand, Fatemeh Daneshfar, Elaheh Sadat Salehi et al. |
| 5 | 572 | 21.4 | [A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection](https://doi.org/10.1109/tpami.2024.3443141) | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 2024-08-14 | Ming Jin, Huan Yee Koh, Qingsong Wen et al. |
| 6 | 437 | 13.2 | [MotionDiffuse: Text-Driven Human Motion Generation With Diffusion Model](https://doi.org/10.1109/tpami.2024.3355414) | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 2024-01-29 | Mingyuan Zhang, Zhongang Cai, Liang Pan et al. |
| 7 | 378 | 11.5 | [Leveraging large language models for predictive chemistry](https://doi.org/10.1038/s42256-023-00788-1) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | 2024-02-06 | Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega‐Guerrero et al. |
| 8 | 377 | 10.7 | [Structured Pruning for Deep Convolutional Neural Networks: A Survey](https://doi.org/10.1109/tpami.2023.3334614) | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 2023-11-28 | Yang He, Lingao Xiao |
| 9 | 374 | 13.3 | [Physics-informed machine learning: A comprehensive review on applications in anomaly detection and condition monitoring](https://doi.org/10.1016/j.eswa.2024.124678) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 2024-07-04 | Yuandi Wu, Brett S. Sicard, S. Andrew Gadsden |
| 10 | 342 | 12.1 | [A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-Modal](https://doi.org/10.1109/tpami.2024.3417451) | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 2024-06-28 | Ke Liang, Lingyuan Meng, Meng Liu et al. |

Archive counts by publication year: a paper counts when its title, abstract or keywords name the equation, or when OpenAlex's full-text search finds it there (searched for the last 30 days' papers and, by a pass over the past years, for older ones). Names added to the list later are looked for in older papers as the archive is read again.

*Papers mentioning Gaussian process regression, newest first.*

| Date | Title | Journal | Authors | Affiliations |
|---|---|---|---|---|
| 2026-11-01 | [A subnational approach to energy transition costs in Venezuela](https://doi.org/10.1016/j.jup.2026.102369) | [Utilities Policy](https://scholar4ai.com/journals/utilities-policy.md) | Fabiola González Pérez, Esteban Ricardo García Clavel, Thomas Stringer | Polytechnique Montréal; Universitat Politècnica de Catalunya |
| 2026-10-27 | [Meta additive model: interpretable sparse learning with auto weighting](https://doi.org/10.1007/s10618-026-01274-z) | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | Xuelin Zhang, Xinyue Liu, Lingjuan Wu et al. | Huazhong Agricultural University; Dongfeng Motor (China) |
| 2026-10-26 | [ChiSao: A fully GPU-resident optimizer for cataloguing the modes of multimodal black-box functions](https://doi.org/10.1016/j.swevo.2026.102574) | [Swarm and Evolutionary Computation](https://scholar4ai.com/journals/swarm-and-evolutionary-computation.md) | Ira Wolfson | Braude College of Engineering Karmiel |
| 2026-10-26 | [Local increment inference for time-inhomogeneous drift in Gaussian processes](https://doi.org/10.1007/s42081-026-00383-9) | [Japanese Journal of Statistics and Data Science](https://scholar4ai.com/journals/japanese-journal-of-statistics-and-data-science.md) | Yasutaka Shimizu | Waseda University |
| 2026-10-24 | [Change-aware contrastive learning framework for dynamic multi-objective optimization](https://doi.org/10.1016/j.ins.2026.124331) | [Information Sciences](https://scholar4ai.com/journals/information-sciences.md) | Hao Lin, Feng Shen, Qingbo Hao et al. | Inner Mongolia University of Technology; Tianjin University of Technology |
| 2026-10-23 | [Truck appointment quota optimization for an inland logistics port: Balancing port operational efficiency and planning stability for trucking companies](https://doi.org/10.1016/j.cie.2026.112476) | [Computers & Industrial Engineering](https://scholar4ai.com/journals/computers-industrial-engineering.md) | Yating Li, Yongman Zhao, Shi Qiang Liu et al. | Shihezi University; Xinjiang Production and Construction Corps; Ministry of Agriculture and Rural Affairs; … |
| 2026-10-23 | [Generalized and scalable deep Gaussian process emulation](https://doi.org/10.1016/j.csda.2026.108485) | [Computational Statistics & Data Analysis](https://scholar4ai.com/journals/computational-statistics-data-analysis.md) | Deyu Ming, Daniel Williamson | University College London; University of Exeter |
| 2026-10-23 | [Global–local shrinkage priors for modeling random effects in multivariate spatial small area estimation](https://doi.org/10.1016/j.spasta.2026.101062) | [Spatial Statistics](https://scholar4ai.com/journals/spatial-statistics.md) | Shushi Nishina, Takahiro Onizuka, Shintaro Hashimoto | Hiroshima University; Chiba University |
| 2026-10-23 | [An infinite BART model](https://doi.org/10.1007/s11222-026-11006-4) | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | Marco Battiston, Yu Luo | Lancaster University; King's College London |
| 2026-10-22 | [A Maintenance Policy for a Partially Observed/Observable Parallel System with Two Components Subjected to Stochastically Dependent Degradation Processes](https://doi.org/10.1007/s11009-026-10346-y) | [Methodology And Computing In Applied Probability](https://scholar4ai.com/journals/methodology-and-computing-in-applied-probability.md) | Nicola Esposito, Lucía Bautista, Massimiliano Giorgio et al. | Scuola Superiore Meridionale; Universidad de Extremadura; University of Naples Federico II |
| 2026-10-22 | [A stage-based adaptive surrogate management algorithm for expensive constrained multi-objective optimization](https://doi.org/10.1016/j.eswa.2026.134842) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Cheng Xu, Haiping Chen, Yongkuan Yang et al. | Xiamen University of Technology; Contemporary Amperex Technology Co., Limited.; Anhui University |
| 2026-10-22 | [Testing the goodness-of-fit of a functional autoregressive model](https://doi.org/10.1016/j.jmva.2026.105726) | [Journal of Multivariate Analysis](https://scholar4ai.com/journals/journal-of-multivariate-analysis.md) | Wenceslao González–Manteiga, M. D. Ruiz‐Medina, M. Febrero–Bande | Universidade de Santiago de Compostela; Universidad de Granada |
| 2026-10-20 | [Fast simulation of Gaussian random fields with flexible correlation models in Euclidean spaces](https://doi.org/10.1007/s11222-026-11002-8) | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | Moreno Bevilacqua, Xavier Emery, Francisco Cuevas‐Pacheco | Ca' Foscari University of Venice; Adolfo Ibáñez University; Advanced Mining Technology Center; … |
| 2026-10-15 | [Metropolis-scale resilient and trustworthy traffic flow inference using multi-source data](https://doi.org/10.1016/j.trc.2026.106083) | [Transportation Research Part C Emerging Technologies](https://scholar4ai.com/journals/transportation-research-part-c-emerging-technologies.md) | Qishen Zhou, Yifan Zhang, Michail Makridis et al. | Jilin University; City University of Hong Kong; City University of Hong Kong (Dongguan); … |
| 2026-10-14 | [Semi-parametric Bayesian inference to shape-restricted partially linear additive mixed-effects negative binomial models for longitudinal data](https://doi.org/10.1007/s42952-026-00394-y) | [Journal of the Korean Statistical Society](https://scholar4ai.com/journals/journal-of-the-korean-statistical-society.md) | Miaojie Xia, Ya Liu, Jiang Du | Beijing University of Technology |
| 2026-10-14 | [Increasing domain asymptotics for covariate-based nonparametric Bayesian intensity estimation with Gaussian and Besov–Laplace priors](https://doi.org/10.1016/j.jmva.2026.105715) | [Journal of Multivariate Analysis](https://scholar4ai.com/journals/journal-of-multivariate-analysis.md) | Patric Dolmeta, Matteo Giordano | University of Turin |
| 2026-10-14 | [A comparison of mixed-models for the analysis of non-linear longitudinal data: application to late-life cognitive trajectories](https://doi.org/10.1186/s12874-026-02967-1) | [BMC Medical Research Methodology](https://scholar4ai.com/journals/bmc-medical-research-methodology.md) | Maude Wagner, Don Hedeker, Tianhao Wang et al. | Rush University Medical Center |
| 2026-10-13 | [Predicting individual stock crashes via extreme return probabilities: Machine learning with Bayesian optimization](https://doi.org/10.1016/j.jbankfin.2026.107737) | [Journal of Banking & Finance](https://scholar4ai.com/journals/journal-of-banking-finance.md) | Zihao Chen, Haitao Li, Guoliang Ma et al. | Iowa State University; Cheung Kong Graduate School of Business; Xiamen University |
| 2026-10-12 | [Gaussian Splatting for real-time scene reconstruction: Methods, systems, and trade-offs](https://doi.org/10.1016/j.patcog.2026.115120) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Ashim Dahal, Rabab Abdelfattah, Nick Rahimi | University of Southern Mississippi |
| 2026-10-11 | [INLA-RF: A hybrid modeling strategy for spatio-temporal environmental data](https://doi.org/10.1016/j.csda.2026.108481) | [Computational Statistics & Data Analysis](https://scholar4ai.com/journals/computational-statistics-data-analysis.md) | Mario Figueira, Michela Cameletti, Luca Patelli | Universitat de València; University of Bergamo |
| 2026-10-11 | [A Spectral Clustering-Type Algorithm for the Consistent Estimation of the Hurst Distribution in Moderately High Dimensions](https://doi.org/10.1007/s13171-026-00462-9) | [Sankhya A](https://scholar4ai.com/journals/sankhya-a.md) | Patrice Abry, Gustavo Didier, Oliver Orejola et al. | École Normale Supérieure de Lyon; Centre National de la Recherche Scientifique; Laboratoire de Physique de l'ENS de Lyon; … |
| 2026-10-10 | [Perturbation bounds for extreme quantiles of Gaussian maxima](https://doi.org/10.1016/j.spl.2026.110989) | [Statistics & Probability Letters](https://scholar4ai.com/journals/statistics-probability-letters.md) | Zoubir Kenioua | Amar Telidji University of Laghouat |
| 2026-10-09 | [Bayesian spatio-temporal disaggregation modeling using a diffusion-SPDE approach: A case study of Aerosol Optical Depth in India](https://doi.org/10.1016/j.spasta.2026.101058) | [Spatial Statistics](https://scholar4ai.com/journals/spatial-statistics.md) | Fernando Rodriguez Avellaneda, Paula Moraga | King Abdullah University of Science and Technology |
| 2026-10-09 | [ROC curve analysis for functional markers](https://doi.org/10.1016/j.jmva.2026.105718) | [Journal of Multivariate Analysis](https://scholar4ai.com/journals/journal-of-multivariate-analysis.md) | Ana M. Bianco, Graciela Boente, Juan Carlos Pardo–Fernández | Consejo Nacional de Investigaciones Científicas y Técnicas; Universidad de Buenos Aires; Universidade de Vigo |
| 2026-10-08 | [Online global HD map updating and construction based on road geometry](https://doi.org/10.1016/j.imavis.2026.106238) | [Image and Vision Computing](https://scholar4ai.com/journals/image-and-vision-computing.md) | Chunchit Siu, Taohong Zhu, Ke Song et al. | Sun Yat-sen University |
| 2026-10-08 | [Dimension reduction of multivariate densities in Bayes spaces](https://doi.org/10.1016/j.jmva.2026.105713) | [Journal of Multivariate Analysis](https://scholar4ai.com/journals/journal-of-multivariate-analysis.md) | Adéla Czolková, Karel Hron, Sonja Greven | Palacký University Olomouc; Humboldt-Universität zu Berlin |
| 2026-10-08 | [Gaussian process-based mortality monitoring using multivariate cumulative sum procedures](https://doi.org/10.1016/j.insmatheco.2026.103308) | [Insurance Mathematics and Economics](https://scholar4ai.com/journals/insurance-mathematics-and-economics.md) | Karim Barigou, Stéphane Loisel, Yahia Salhi et al. | UCLouvain; Conservatoire National des Arts et Métiers; Laboratoire interdisciplinaire de recherches en sciences de l'action; … |

## Cite this page

Scholar4AI. (2026). Gaussian process regression. Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/equations/gaussian-process-regression

```bibtex
@misc{scholar4ai-equations-gaussian-process-regression,
  author = {{Scholar4AI}},
  title = {Gaussian process regression},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/equations/gaussian-process-regression}},
  urldate = {2026-11-05},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).