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Topic · Artificial Intelligence

Gaussian Processes and Bayesian Inference

Gaussian Processes and Bayesian Inference is a research topic within Artificial Intelligence. Science Explorer counts 8.6k research works in it since 1951. 22.9% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the application of Gaussian Processes in machine learning, covering topics such as variational inference, sparse regression, Bayesian inference, deep learning, and probabilistic models. It also explores the use of Gaussian Processes for nonparametric methods, time series modelling, and handling big data.

  • Gaussian Processes
  • Machine Learning
  • Variational Inference
  • Sparse Regression
  • Bayesian Inference
  • Deep Learning
  • Probabilistic Models
  • Nonparametric Methods
  • Time Series Modelling
  • Big Data
Research works
8.6k
fractional, since 1951
In the world top 10%
2k
per year above
Top-10% rate
22.9%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+41%
the tick is no change

Which countries lead Gaussian Processes and Bayesian Inference research?

By volume, the United States and China publish the most (603 and 341 works in 2022–2025).

By volume, 2022–2025

  1. 1 United States 603 works
  2. 2 China 341 works
  3. 3 United Kingdom 145 works
  4. 4 Germany 128 works
  5. 5 France 113 works
  6. 6 Italy 68 works
  7. 7 Japan 66 works
  8. 8 India 53 works
  9. 9 Canada 51 works
  10. 10 South Korea 44 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

United States: 37.4%China: 21.2%United Kingdom: 9.0%Germany: 7.9%6 others listed: 24.5%37%largest
United States603 · 37.4%China341 · 21.2%United Kingdom145 · 9.0%Germany128 · 7.9%6 others listed395 · 24.5%

Shares of the rows listed above, not of the whole node.

Which institutions lead Gaussian Processes and Bayesian Inference research?

By volume in 2022–2025, Imperial College London publishes the most Gaussian Processes and Bayesian Inference research, followed by The University of Texas at Austin and Shanghai Jiao Tong University.

By volume, 2022–2025

  1. 1 Imperial College London United Kingdom 13 works
  2. 2 The University of Texas at Austin United States 13 works
  3. 3 Shanghai Jiao Tong University China 12 works
  4. 4 University of Cambridge United Kingdom 12 works
  5. 5 University of Michigan United States 12 works
  6. 6 Georgia Institute of Technology United States 11 works
  7. 7 Technical University of Munich Germany 11 works
  8. 8 Centre National de la Recherche Scientifique France 11 works
  9. 9 Stony Brook University United States 11 works
  10. 10 Stanford University United States 11 works

Who are the leading researchers in Gaussian Processes and Bayesian Inference?

The most-cited researchers publishing on Gaussian Processes and Bayesian Inference include Yoshua Bengio, Robert Tibshirani and Stanley Osher.

  1. 1 Yoshua Bengio 17k citations
  2. 2 Robert Tibshirani 13k citations
  3. 3 Stanley Osher 8k citations
  4. 4 George Em Karniadakis 6.3k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Gaussian Processes and Bayesian Inference research done?

The largest centres of Gaussian Processes and Bayesian Inference research in 2022–2025 are Beijing (China), London (United Kingdom), Shanghai (China) and Paris (France).

Largest cities, 2022–2025

  1. 1 Beijing China 72 works
  2. 2 London United Kingdom 36 works
  3. 3 Shanghai China 34 works
  4. 4 Paris France 33 works
  5. 5 Xi'an China 29 works
  6. 6 Tokyo Japan 27 works
  7. 7 New York United States 23 works
  8. 8 Seoul South Korea 21 works
  9. 9 Nanjing China 20 works
  10. 10 Munich Germany 19 works
See Gaussian Processes and Bayesian Inference on the map

Where is the best place to study Gaussian Processes and Bayesian Inference?

Among universities, judged by research, Technical University of Munich, Georgia Institute of Technology and University of Cambridge score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 25.79%fractional works in this node (log) →share in the world top 10% →Technical University of Munich: 11, 21.8%Georgia Institute of Technology: 11, 22.1%University of Cambridge: 12, 34.6%University of Michigan: 12, 27.7%Delft University of Technology: 10, 20.3%Imperial College London: 13, 13.7%The University of Texas at Austin: 13, 19.1%University of Chicago: 9, 38.1%Shanghai Jiao Tong University: 12, 33.2%Massachusetts Institute of Technology: 9, 27.3%University of Cambri…University of MichiganGeorgia Institute of…Technical University…
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1Technical University of Munich Germany 65.321.8%7.2×11 +300.3%
2Georgia Institute of Technology United States 61.422.1%9.3×11 +68.3%
3University of Cambridge United Kingdom 61.134.6%5.5×12 -0.3%
4University of Michigan United States 59.027.7%4.4×12 +227.1%
5Delft University of Technology Netherlands 57.820.3%7.4×10 +165.0%
6Imperial College London United Kingdom 57.313.7%7.5×13 +21.8%
7The University of Texas at Austin United States 53.619.1%8.0×13 -19.3%
8University of Chicago United States 50.238.1%5.7×9 +33.1%
9Shanghai Jiao Tong University China 50.033.2%2.9×12 +42.2%
10Massachusetts Institute of Technology United States 49.727.3%7.6×9 +13.2%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Gaussian Processes and Bayesian Inference research growing?

Output in 2018–2022 was 41% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Gaussian Processes and Bayesian Inference.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.