Most cited papers in Markov Chains and Monte Carlo Methods
What are the most cited papers in Markov Chains and Monte Carlo Methods?
The most cited paper in Markov Chains and Monte Carlo Methods is "Monte Carlo sampling methods using Markov chains and their applications" (W. Keith Hastings, 1970), with 15,456 citations. Next come "emcee : The MCMC Hammer" (12,323) and "General Methods for Monitoring Convergence of Iterative Simulations" (6,193).
Showing 50 of 100 papers. Every paper with more than 709 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Monte Carlo sampling methods using Markov chains and their applications W. Keith Hastings · Biometrika | Apr 1970 | 15,456 |
| 2 | emcee : The MCMC Hammer Daniel Foreman-Mackey, David W. Hogg, Dustin Lang et al. · Publications of the Astronomical Society of the Pacific | Mar 2013 | 12,323 |
| 3 | General Methods for Monitoring Convergence of Iterative Simulations Stephen P. Brooks, Andrew Gelman · Journal of Computational and Graphical Statistics | Dec 1998 | 6,193 |
| 4 | Reversible jump Markov chain Monte Carlo computation and Bayesian model determination Peter J. Green · Biometrika | 1995 | 5,963 |
| 5 | Monte Carlo Statistical Methods Hoon Kim, Christian P. Robert, George Casella · Technometrics | Nov 2000 | 5,603 |
| 6 | Understanding the Metropolis-Hastings Algorithm Siddhartha Chib, Edward Greenberg · The American Statistician | Nov 1995 | 3,756 |
| 7 | A Learning Algorithm for Boltzmann Machines* David H. Ackley, Geoffrey E. Hinton, Terrence J. Sejnowski · Cognitive Science | 1985 | 3,328 |
| 8 | The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo Matthew D. Homan, Andrew Gelman · arXiv (Cornell University) | 2014 | 3,272 |
| 9 | Ensemble samplers with affine invariance Jonathan B. Goodman, Jonathan Weare · Communications in Applied Mathematics and Computational Science | 31 Jan 2010 | 3,183 |
| 10 | A well-conditioned estimator for large-dimensional covariance matrices Olivier Ledoit, Michael Wolf · Journal of Multivariate Analysis | 16 Jul 2003 | 3,093 |
| 11 | Adapted solution of a backward stochastic differential equation Étienne Pardoux, Shigē Péng · Systems & Control Letters | 1990 | 2,817 |
| 12 | An Adaptive Metropolis Algorithm Heikki Haario, Eero Saksman, Johanna Tamminen · Bernoulli | Apr 2001 | 2,788 |
| 13 | The Complexity of Computing the Permanent Leslie Gabriel Valiant · Theoretical Computer Science | 1979 | 2,777 |
| 14 | Optimized Monte Carlo data analysis Alan M. Ferrenberg, Robert H. Swendsen · Physical Review Letters | 18 Sep 1989 | 2,619 |
| 15 | Collective Monte Carlo Updating for Spin Systems Ulli Wolff · Physical Review Letters | 23 Jan 1989 | 2,588 |
| 16 | A new algorithm for Monte Carlo simulation of Ising spin systems Alfred B. Bortz, M. H. Kalos, Joel L. Lebowitz · Journal of Computational Physics | 1975 | 2,425 |
| 17 | Statistical Theory of Equations of State and Phase Transitions. II. Lattice Gas and Ising Model T. D. Lee, Chongming Yang · Physical Review | Aug 1952 | 2,254 |
| 18 | Markov Chain Sampling Methods for Dirichlet Process Mixture Models Radford M. Neal · Journal of Computational and Graphical Statistics | Jun 2000 | 2,222 |
| 19 | Finite size scaling analysis of ising model block distribution functions K. Binder · The European Physical Journal B | Jun 1981 | 2,161 |
| 20 | Robust Stochastic Approximation Approach to Stochastic Programming Arkadi S. Nemirovski, Anatoli Juditsky, Guanghui Lan et al. · SIAM Journal on Optimization | 2009 | 2,115 |
| 21 | Particle Markov Chain Monte Carlo Methods Christophe Andrieu, Arnaud Doucet, Roman Holenstein · Journal of the Royal Statistical Society Series B (Statistical Methodology) | 20 May 2010 | 2,076 |
| 22 | Practical Markov Chain Monte Carlo Charles J. Geyer · Statistical Science | Nov 1992 | 2,071 |
| 23 | Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review Mary Kathryn Cowles, Bradley P. Carlin · Journal of the American Statistical Association | Jun 1996 | 1,997 |
| 24 | On Gibbs sampling for state space models Chris Carter, Robert Kohn · Biometrika | 1994 | 1,994 |
| 25 | Optimization flow control. I. Basic algorithm and convergence Steven H. Low, D.E. Lapsely · IEEE/ACM Transactions on Networking | 1999 | 1,982 |
| 26 | Simulated Tempering: A New Monte Carlo Scheme Enzo Marinari, Giorgio Parisi · Europhysics Letters (EPL) | 15 Jul 1992 | 1,857 |
| 27 | Sequential Monte Carlo Methods for Dynamic Systems Jun S. Liu, Rong Chen · Journal of the American Statistical Association | Sep 1998 | 1,802 |
| 28 | Random sampling with a reservoir Jeffrey Scott Vitter · ACM Transactions on Mathematical Software | Mar 1985 | 1,793 |
| 29 | Markov Chain Monte Carlo in Practice Mauro Gasparini, W. R. Gilks, Sylvia T. Richardson et al. · Technometrics | Aug 1997 | 1,769 |
| 30 | Weak convergence and optimal scaling of random walk Metropolis algorithms Gareth O. Roberts, Susan A. Gelman, Walter R. Gilks · The Annals of Applied Probability | Feb 1997 | 1,748 |
| 31 | Partition function of the Eight-Vertex lattice model R. J. Baxter · Annals of Physics | Mar 1972 | 1,742 |
| 32 | Bayesian Computation Via the Gibbs Sampler and Related Markov Chain Monte Carlo Methods A. F. M. Smith, Gareth O. Roberts · Journal of the Royal Statistical Society Series B (Statistical Methodology) | Sep 1993 | 1,732 |
| 33 | Nested sampling for general Bayesian computation John Skilling · Bayesian Analysis | Dec 2006 | 1,717 |
| 34 | Rank-Normalization, Folding, and Localization: An Improved Rˆ for Assessing Convergence of MCMC (with Discussion) Aki Vehtari, Andrew Gelman, Daniel Simpson et al. · Bayesian Analysis | 3 Jul 2020 | 1,671 |
| 35 | Critical Exponents in 3.99 Dimensions Kenneth G. Wilson, Michael Ellis Fisher · Physical Review Letters | 24 Jan 1972 | 1,660 |
| 36 | Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems Tina Toni, David Welch, Natalja Strelkowa et al. · Journal of The Royal Society Interface | 9 Jul 2008 | 1,625 |
| 37 | DRAM: Efficient adaptive MCMC Heikki Haario, Marko Laine, Antonietta Mira et al. · Statistics and Computing | 12 Oct 2006 | 1,569 |
| 38 | Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods Mark Girolami, Ben Calderhead · Journal of the Royal Statistical Society Series B (Statistical Methodology) | Mar 2011 | 1,568 |
| 39 | Gibbs Sampling Methods for Stick-Breaking Priors Hemant Ishwaran, Lancelot F. James · Journal of the American Statistical Association | Mar 2001 | 1,565 |
| 40 | Monte Carlo Sampling Methods Using Markov Chains and Their Applications W. K. Hastings · Biometrika | Apr 1970 | 1,550 |
| 41 | Bayesian Learning via Stochastic Gradient Langevin Dynamics Max Welling, Yee Whye Teh | 14 May 2012 | 1,477 |
| 42 | Slice sampling Radford M. Neal · The Annals of Statistics | Jun 2003 | 1,380 |
| 43 | Approximate Bayesian Computation (ABC) in practice Katalin Csilléry, Michaël G. B. Blum, Oscar E. Gaggiotti et al. · Trends in Ecology & Evolution | 19 May 2010 | 1,365 |
| 44 | Exact solution of a 1D asymmetric exclusion model using a matrix formulation Bernard Derrida, M. R. Evans, Vincent Hakim et al. · Journal of Physics A Mathematical and General | 7 Apr 1993 | 1,329 |
| 45 | The statistics of dimers on a lattice Piet Kasteleyn · Physica | Dec 1961 | 1,299 |
| 46 | Very fast simulated re-annealing Lester Ingber · Mathematical and Computer Modelling | 1989 | 1,298 |
| 47 | Bayesian Model Choice: Asymptotics and Exact Calculations Alan E. Gelfand, Dipak K. Dey · Journal of the Royal Statistical Society Series B (Statistical Methodology) | Sep 1994 | 1,251 |
| 48 | On Choosing and Bounding Probability Metrics Alison L. Gibbs, Francis Edward Su · International Statistical Review | Dec 2002 | 1,219 |
| 49 | Multicanonical algorithms for first order phase transitions Bernd A. Berg, Thomas Neuhaus · Physics Letters B | Sep 1991 | 1,206 |
| 50 | Accelerating Markov Chain Monte Carlo Simulation by Differential Evolution with Self-Adaptive Randomized Subspace Sampling Jasper Alexander Vrugt, Cajo J. F. ter Braak, Cees G. H. Diks et al. · International Journal of Nonlinear Sciences and Numerical Simulation | 2009 | 1,191 |
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.