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Bayesian Methods and Mixture Models

Bayesian Methods and Mixture Models is a research topic within Artificial Intelligence. Science Explorer counts 25k research works in it since 1950. 10.0% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the application of mixture models, particularly Gaussian finite mixture models and Dirichlet process mixture models, for model-based clustering, discriminant analysis, density estimation, and unsupervised learning. It explores various inference methods such as Bayesian inference, variational inference, and Markov Chain Monte Carlo for estimating parameters in mixture models. The cluster also delves into the challenges of identifiability, variable selection, and dealing with label switching in the context of mixture models.

  • Mixture Models
  • Clustering
  • Bayesian Inference
  • Dirichlet Process
  • Gaussian Mixture Models
  • Variational Inference
  • Markov Chain Monte Carlo
  • Finite Mixtures
  • Hidden Markov Models
  • Nonparametric Bayesian
Research works
25k
fractional, since 1950
In the world top 10%
2.5k
per year above
Top-10% rate
10.0%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+3%
the tick is no change
Largest producer
United States
33% of the 10 largest, 2022-now
Bayesian Methods and Mixture Models

Which countries lead Bayesian Methods and Mixture Models research?

By volume, the United States and China publish the most (867 and 530 works in 2022-now).

By volume, 2022-now

  1. 1 United States 867 works
  2. 2 China 530 works
  3. 3 France 211 works
  4. 4 Italy 194 works
  5. 5 India 176 works
  6. 6 United Kingdom 163 works
  7. 7 Canada 162 works
  8. 8 Germany 126 works
  9. 9 Japan 113 works
  10. 10 Saudi Arabia 72 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: 33.2%China: 20.3%France: 8.1%Italy: 7.4%6 others listed: 31.1%33%largest
United States867 · 33.2%China530 · 20.3%France211 · 8.1%Italy194 · 7.4%6 others listed812 · 31.1%

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

Which institutions lead Bayesian Methods and Mixture Models research?

By volume in 2022-now, Centre National de la Recherche Scientifique publishes the most Bayesian Methods and Mixture Models research, followed by Russian Academy of Sciences and Sapienza University of Rome.

By volume, 2022-now

  1. 1 Centre National de la Recherche ScientifiqueFrance 73 works
  2. 2 Russian Academy of SciencesRussia 23 works
  3. 3 Sapienza University of RomeItaly 20 works
  4. 4 Duke UniversityUnited States 20 works
  5. 5 The University of Texas at AustinUnited States 18 works
  6. 6 Stanford UniversityUnited States 17 works
  7. 7 Concordia UniversityCanada 16 works
  8. 8 University of CambridgeUnited Kingdom 16 works
  9. 9 University of British ColumbiaCanada 16 works
  10. 10 University of North Carolina at Chapel HillUnited States 16 works

Who are the leading researchers in Bayesian Methods and Mixture Models?

The most-cited researchers publishing on Bayesian Methods and Mixture Models 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

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 Bayesian Methods and Mixture Models?

The most cited paper in Bayesian Methods and Mixture Models is "Regression Shrinkage and Selection Via the Lasso" (Robert John Tibshirani, 1996), with 53,131 citations. Next come "CONFIDENCE LIMITS ON PHYLOGENIES: AN APPROACH USING THE BOOTSTRAP" (41,763) and "Latent dirichlet allocation" (27,026).

Most cited papers
#PaperReleasedCitations
1 Regression Shrinkage and Selection Via the Lasso Robert John Tibshirani · Journal of the Royal Statistical Society Series B (Statistical Methodology) 1996 53,131
2 CONFIDENCE LIMITS ON PHYLOGENIES: AN APPROACH USING THE BOOTSTRAP Joseph Felsenstein · Evolution Open access Jul 1985 41,763
3 Latent dirichlet allocation David M. Blei, Andrew Y. Ng, Michael I. Jordan · Journal of Machine Learning Research Mar 2003 27,026
4 Distribution of the Estimators for Autoregressive Time Series with a Unit Root David Alan Dickey, Wayne Arthur Fuller · Journal of the American Statistical Association Open access Jun 1979 23,250
5 A tutorial on hidden Markov models and selected applications in speech recognition L. R. Rabiner · Proceedings of the IEEE 1989 22,958
All 97 most cited papers, with filters and sorting

Where is Bayesian Methods and Mixture Models research done?

The largest centres of Bayesian Methods and Mixture Models research in 2022-now are Beijing (China), Paris (France), Tokyo (Japan) and Shanghai (China). Among places with at least 20 works in it, it is an unusually large share of all research in Durham.

Largest cities, 2022-now

  1. 1 BeijingChina 101 works
  2. 2 ParisFrance 64 works
  3. 3 TokyoJapan 53 works
  4. 4 ShanghaiChina 45 works
  5. 5 MilanItaly 41 works
  6. 6 LondonUnited Kingdom 41 works
  7. 7 New YorkUnited States 36 works
  8. 8 MoscowRussia 35 works
  9. 9 MontrealCanada 35 works
  10. 10 RomeItaly 35 works

Where it is the local speciality

  1. DurhamUS · 22.0 works4.9×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Bayesian Methods and Mixture Models than the world average.

See Bayesian Methods and Mixture Models on the map

Where is the best place to study Bayesian Methods and Mixture Models?

Among universities, judged by research, Université de Technologie de Compiègne, Duke University 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%25%50%mean 19.91%fractional works in this field (log) →share of work in the world top 10% →Université de Techno…University of Cambri…Stanford UniversityDuke University
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
1Université de Technologie de CompiègneFrance 68.158.0%79.6×8 +347.8%
2Duke UniversityUnited States 60.410.5%7.2×20 +70.6%
3University of CambridgeUnited Kingdom 60.419.1%4.1×16 +46.5%
4Stanford UniversityUnited States 56.914.6%3.4×17 +42.5%
5University of TorontoCanada 56.824.7%2.2×15 +36.4%
6University of Milano-BicoccaItaly 56.416.3%15.9×15 +141.0%
7Sapienza University of RomeItaly 54.48.5%5.3×20 +84.1%
8The University of Texas at AustinUnited States 53.617.1%6.4×18 +0.9%
9University of CataniaItaly 53.226.7%10.1×10 +97.4%
10University of British ColumbiaCanada 49.63.6%4.0×16 +63.8%

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 Bayesian Methods and Mixture Models, country by country

Is Bayesian Methods and Mixture Models research growing?

Output in 2022-2025 was 3% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Bayesian Methods and Mixture Models.

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 Bayesian Methods and Mixture Models as a percentage of every work published that year, 0.015% in 2024.

still counting0.020%0.040%0.060%Bayesian Methods and Mixture Models0.016% in 202519801990200020102020

Fractional works of Bayesian Methods and Mixture Models 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%Bayesian Methods and Mixture Models10.0% 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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