Most cited papers in Gaussian Processes and Bayesian Inference
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).
Showing 50 of 99 papers. Every paper with more than 1,067 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 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) | 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 | 24 May 2021 | 8,059 |
| 6 | Stan : A Probabilistic Programming Language Bob Carpenter, Andrew Gelman, Matthew D. Hoffman et al. · Journal of Statistical Software | 2017 | 7,589 |
| 7 | Taking the Human Out of the Loop: A Review of Bayesian Optimization Bobak Shahriari, Kevin Swersky, Ziyu Wang et al. · Proceedings of the IEEE | 10 Dec 2015 | 6,252 |
| 8 | Denoising Diffusion Probabilistic Models Ho, Jonathan, Ajay N. Jain, Pieter Abbeel · arXiv (Cornell University) | 19 Jun 2020 | 5,696 |
| 9 | Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC Aki Vehtari, Andrew Gelman, Jonah Gabry · Statistics and Computing | 30 Aug 2016 | 5,670 |
| 10 | Practical Bayesian Optimization of Machine Learning Algorithms Jasper Snoek, Hugo Larochelle, Ryan Prescott Adams · arXiv (Cornell University) | 13 Jun 2012 | 5,667 |
| 11 | Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations Håvard Rue, Sara Martino, Nicolas Chopin · Journal of the Royal Statistical Society Series B (Statistical Methodology) | Apr 2009 | 5,508 |
| 12 | Gaussian Processes in Machine Learning Carl Edward Rasmussen · Lecture notes in computer science | 2004 | 5,379 |
| 13 | Discovering governing equations from data by sparse identification of nonlinear dynamical systems Steven L. Brunton, Joshua L. Proctor, J. Nathan Kutz · Proceedings of the National Academy of Sciences | 28 Mar 2016 | 4,849 |
| 14 | Bayesian Interpolation David Mackay · Neural Computation | May 1992 | 4,436 |
| 15 | Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond Christopher K. I. Williams · Journal of the American Statistical Association | Jun 2003 | 4,332 |
| 16 | Bayesian Calibration of Computer Models Marc C. Kennedy, Anthony O’Hagan · Journal of the Royal Statistical Society Series B (Statistical Methodology) | Sep 2001 | 4,303 |
| 17 | Variational Inference: A Review for Statisticians David M. Blei, Alp Kucukelbir, Jon D. McAuliffe · Journal of the American Statistical Association | 27 Feb 2017 | 3,893 |
| 18 | An Introduction to Variational Methods for Graphical Models Michael I. Jordan, Zoubin Ghahramani, Tommi Jaakkola et al. · Machine Learning | Nov 1999 | 3,812 |
| 19 | Asymptotically Efficient Rank Invariant Test Procedures Richard Peto, Julian Peto · Journal of the Royal Statistical Society Series A (General) | 1972 | 3,431 |
| 20 | Approximate Bayesian Computation in Population Genetics Mark Beaumont, Wenyang Zhang, David J. Balding · Genetics | Dec 2002 | 3,126 |
| 21 | A Tutorial on the Cross-Entropy Method Pieter-Tjerk de Boer, Dirk P. Kroese, Shie Mannor et al. · Annals of Operations Research | Feb 2005 | 3,111 |
| 22 | Estimating Continuous Distributions in Bayesian Classifiers George H. John, Pat Langley · arXiv (Cornell University) | 20 Feb 2013 | 2,923 |
| 23 | A Reliable Data-Based Bandwidth Selection Method for Kernel Density Estimation Simon J. Sheather, M. C. Jones · Journal of the Royal Statistical Society Series B (Statistical Methodology) | Jul 1991 | 2,706 |
| 24 | Hierarchical Mixtures of Experts and the EM Algorithm Michael I. Jordan, Robert A. Jacobs · Neural Computation | Mar 1994 | 2,704 |
| 25 | Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Yarin Gal, Zoubin Ghahramani · arXiv (Cornell University) | 6 Jun 2015 | 2,679 |
| 26 | An Introduction to Variational Autoencoders Diederik P. Kingma, Max Welling · Foundations and Trends® in Machine Learning | 28 Nov 2019 | 2,652 |
| 27 | Stochastic Backpropagation and Approximate Inference in Deep Generative Models Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra · arXiv (Cornell University) | 16 Jan 2014 | 2,638 |
| 28 | Generating samples under a Wright–Fisher neutral model of genetic variation Richard R. Hudson · Bioinformatics | Feb 2002 | 2,592 |
| 29 | Probabilistic programming in Python using PyMC3 John Salvatier, Thomas V. Wiecki, Christopher Fonnesbeck · PeerJ Computer Science | 6 Apr 2016 | 2,578 |
| 30 | Conditional Random Fields as Recurrent Neural Networks Shuai Zheng, Sadeep Jayasumana, Bernardino Romera‐Paredes et al. | Dec 2015 | 2,450 |
| 31 | An Introduction to MCMC for Machine Learning Christophe Andrieu, Nando de Freitas, Arnaud Doucet et al. · Machine Learning | 2003 | 2,448 |
| 32 | Dynamic topic models David M. Blei, John Lafferty | 2006 | 2,424 |
| 33 | Understanding deep learning (still) requires rethinking generalization Chiyuan Zhang, Samy Bengio, Moritz Hardt et al. · Communications of the ACM | 22 Feb 2021 | 2,392 |
| 34 | Explaining the Gibbs Sampler George Casella, Edward I. George · The American Statistician | Aug 1992 | 2,377 |
| 35 | Fast Kd-Trees for the Kullback-Leibler Divergence and Other Decomposable Bregman Divergences Pham, Tuyen, Wagner, Hubert · Leibniz international proceedings in informatics | 2025 | 2,360 |
| 36 | Multivariate stochastic approximation using a simultaneous perturbation gradient approximation James C. Spall · IEEE Transactions on Automatic Control | Mar 1992 | 2,326 |
| 37 | dynesty: a dynamic nested sampling package for estimating Bayesian posteriors and evidences Joshua S Speagle · Monthly Notices of the Royal Astronomical Society | 30 Jan 2020 | 2,320 |
| 38 | Sliced Inverse Regression for Dimension Reduction Ker-Chau Li · Journal of the American Statistical Association | Jun 1991 | 2,123 |
| 39 | Model compression Cristian Buciluǎ, Rich Caruana, Alexandru Niculescu-Mizil | 20 Aug 2006 | 2,122 |
| 40 | Automatic Differentiation in Machine Learning: a Survey Atılım Güneş Baydin, Barak A. Pearlmutter, Alexey Radul et al. · Maynooth University ePrints and eTheses Archive (Maynooth University) | 2015 | 2,093 |
| 41 | Information-theoretic metric learning Jason V. Davis, Brian Kulis, Prateek Jain et al. | 20 Jun 2007 | 2,059 |
| 42 | Probabilistic machine learning and artificial intelligence Zoubin Ghahramani · Nature | 26 May 2015 | 2,047 |
| 43 | The Gaussian Mixture Probability Hypothesis Density Filter Ba‐Ngu Vo, Wing‐Kin Ma · IEEE Transactions on Signal Processing | 18 Oct 2006 | 1,952 |
| 44 | The BUGS project: Evolution, critique and future directions David J. Lunn, David J. Spiegelhalter, Andrew C. Thomas et al. · Statistics in Medicine | 24 Jul 2009 | 1,901 |
| 45 | Inverse problems: A Bayesian perspective Andrew M. Stuart · Acta Numerica | May 2010 | 1,892 |
| 46 | Tutorial on maximum likelihood estimation In Jae Myung · Journal of Mathematical Psychology | Feb 2003 | 1,830 |
| 47 | Marginal Likelihood from the Gibbs Output Siddhartha Chib · Journal of the American Statistical Association | Dec 1995 | 1,826 |
| 48 | A Unifying View of Sparse Approximate Gaussian Process Regression Joaquin Quiñonero-Candela, Carl Edward Rasmussen | Dec 2005 | 1,775 |
| 49 | AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models David A. Fournier, Hans Julius Skaug, Johnoel Ancheta et al. · Optimization methods & software | 3 Oct 2011 | 1,766 |
| 50 | A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions Eric R. Schulz, Maarten Speekenbrink, Andreas Krause · Journal of Mathematical Psychology | 26 May 2018 | 1,753 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.