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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. 10.2% 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%
879
per year above
Top-10% rate
10.2%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+18%
the tick is no change
Largest producer
United States
36% of the 10 largest, 2022-now
Gaussian Processes and Bayesian Inference

Which countries lead Gaussian Processes and Bayesian Inference research?

By volume, the United States and China publish the most (766 and 494 works in 2022-now).

By volume, 2022-now

  1. 1 United States 766 works
  2. 2 China 494 works
  3. 3 United Kingdom 184 works
  4. 4 Germany 162 works
  5. 5 France 135 works
  6. 6 Italy 87 works
  7. 7 Japan 83 works
  8. 8 India 75 works
  9. 9 Canada 64 works
  10. 10 South Korea 55 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: 36.4%China: 23.5%United Kingdom: 8.7%Germany: 7.7%6 others listed: 23.7%36%largest
United States766 · 36.4%China494 · 23.5%United Kingdom184 · 8.7%Germany162 · 7.7%6 others listed500 · 23.7%

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

Which institutions lead Gaussian Processes and Bayesian Inference research?

By volume in 2022-now, Centre National de la Recherche Scientifique publishes the most Gaussian Processes and Bayesian Inference research, followed by Chinese Academy of Sciences and University of Michigan.

By volume, 2022-now

  1. 1 Centre National de la Recherche ScientifiqueFrance 40 works
  2. 2 Chinese Academy of SciencesChina 17 works
  3. 3 University of MichiganUnited States 17 works
  4. 4 Helmholtz Association of German Research CentresGermany 17 works
  5. 5 Imperial College LondonUnited Kingdom 17 works
  6. 6 The University of Texas at AustinUnited States 17 works
  7. 7 Stanford UniversityUnited States 17 works
  8. 8 Shanghai Jiao Tong UniversityChina 15 works
  9. 9 University of CambridgeUnited Kingdom 15 works
  10. 10 Georgia Institute of TechnologyUnited States 14 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, Geoffrey E. Hinton and Robert John Tibshirani.

  1. 1 Yoshua Bengio Canada 476k citations
  2. 2 Geoffrey E. Hinton Canada 454k citations
  3. 3 Robert John Tibshirani United States 396k citations
  4. 4 Karl John Friston United Kingdom 310k citations
  5. 5 Donald B. Rubin United States 301k citations
  6. 6 Trevor Hastie United States 301k citations
  7. 7 Xiaogang Wang China 260k citations
  8. 8 Yann LeCun United States 256k citations
  9. 9 Vladimir N. Vapnik United States 227k citations
  10. 10 Li Fei-Fei United States 225k citations

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

What are the most cited papers in Gaussian Processes and Bayesian Inference?

The most cited paper in Gaussian Processes and Bayesian Inference is "Maximum Likelihood from Incomplete Data Via the EM Algorithm" (A. P. Dempster, N. M. Laird, Donald B. Rubin, 1977), with 50,034 citations. Next come "Auto-Encoding Variational Bayes" (15,541) and "Understanding the difficulty of training deep feedforward neural networks" (12,654).

Most cited papers
#PaperReleasedCitations
1 Maximum Likelihood from Incomplete Data Via the EM Algorithm A. P. Dempster, N. M. Laird, Donald B. Rubin · Journal of the Royal Statistical Society Series B (Statistical Methodology) Sep 1977 50,034
2 Auto-Encoding Variational Bayes Diederik P. Kingma, Max Welling · UvA-DARE (University of Amsterdam) PreprintOpen access 20 Dec 2013 15,541
3 Understanding the difficulty of training deep feedforward neural networks Xavier Glorot, Yoshua Bengio 2010 12,654
4 A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking M.S. Arulampalam, Simon R. Maskell, Neil Gordon et al. · IEEE Transactions on Signal Processing 2002 11,594
5 Physics-informed machine learning George Em Karniadakis, Ioannis G. Kevrekidis, Lu Lu et al. · Nature Reviews Physics ReviewOpen access 24 May 2021 8,059
All 99 most cited papers, with filters and sorting

Where is Gaussian Processes and Bayesian Inference research done?

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

Largest cities, 2022-now

  1. 1 BeijingChina 96 works
  2. 2 ShanghaiChina 45 works
  3. 3 LondonUnited Kingdom 45 works
  4. 4 ParisFrance 41 works
  5. 5 TokyoJapan 38 works
  6. 6 Xi'anChina 35 works
  7. 7 New YorkUnited States 29 works
  8. 8 NanjingChina 27 works
  9. 9 SeoulSouth Korea 26 works
  10. 10 SingaporeSingapore 25 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, California Institute of Technology, University of Michigan and Stanford University 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%10%20%mean 13.11%fractional works in this field (log) →share of work in the world top 10% →California Institute…University of Cambri…Stanford UniversityUniversity of Michigan
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1California Institute of TechnologyUnited States 71.621.0%13.7×11 -6.6%
2University of MichiganUnited States 71.312.2%4.9×17 +64.7%
3Stanford UniversityUnited States 70.515.6%4.9×17 -25.6%
4University of CambridgeUnited Kingdom 67.816.0%5.5×15 -37.3%
5Imperial College LondonUnited Kingdom 67.67.8%7.4×17 +6.8%
6The University of Texas at AustinUnited States 66.67.7%8.9×17 +21.7%
7Shanghai Jiao Tong UniversityChina 66.316.2%2.3×15 +79.2%
8University of EdinburghUnited Kingdom 61.110.3%6.2×12 +39.2%
9Technical University of MunichGermany 59.614.9%6.4×13 -0.6%
10Georgia Institute of TechnologyUnited States 57.59.4%10.0×14 +0.9%

Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Gaussian Processes and Bayesian Inference, country by country

Is Gaussian Processes and Bayesian Inference research growing?

Output in 2022-2025 was 18% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Gaussian Processes and Bayesian Inference.

still counting19801990200020102020
grewheldshrank

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

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Gaussian Processes and Bayesian Inference as a percentage of every work published that year, 0.0087% in 2024.

still counting0.0050%0.010%0.015%Gaussian Processes and Bayesian Inference0.012% in 202519801990200020102020

Fractional works of Gaussian Processes and Bayesian Inference over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Gaussian Processes and Bayesian Inference10.4% in 2025200020102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

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.

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