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Keyword

Metropolis algorithm

Description: The Metropolis algorithm is a Monte Carlo sampling method that generates states from a target distribution using probabilistic acceptance of proposed changes.
Wikidata ID: wiki/Q910810
OpenAlex ID: keywords/metropolis-algorithm
Inference from Iterative Simulation Using Multiple Sequences
1992 · Andrew Gelman, Donald B. Rubin · Statistical Science · 16,525
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Monte Carlo sampling methods using Markov chains and their applications
1970 · W. K. Hastings · Biometrika · 15,177
Markov Chains for Exploring Posterior Distributions
1994 · Luke Tierney · The Annals of Statistics · 3,461
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Monte Carlo Methods in Statistical Physics
1999 · M E J Newman, G T Barkema · 3,173
Handbook of Markov Chain Monte Carlo
2011 · Steve Brooks, Andrew Gelman, et al. · Chapman & Hall/CRC eBooks · 3,018
MCMC Using Hamiltonian Dynamics
2011 · Radford M. Neal · Chapman & Hall/CRC eBooks · 2,575
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Estimation of small failure probabilities in high dimensions by subset simulation
2001 · Siu-Kui Au, James L. Beck · Probabilistic Engineering Mechanics · 2,418
Simulated Tempering: A New Monte Carlo Scheme
1992 · E Marinari, G Parisi · Europhysics Letters (EPL) · 1,820
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Introduction to Metropolis, Rosenbluth, Rosenbluth, Teller, and Teller (1953) Equations of State Calculations by Fast Computing Machines. J. Chem. Phys.,21, 1087–1092. and Geman and Geman (1984) Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. IEEE Trans. Pattern Anal. Machine Intelligence,6, 721–741.
1997 · Peter J. Huber · Springer series in statistics · 1,551
Monte Carlo Sampling Methods Using Markov Chains and Their Applications
1970 · W. K. Hastings · Biometrika · 1,443
Bayesian Analysis of Stochastic Volatility Models
2002 · Eric Jacquier, Nicholas G Polson, et al. · Journal of Business and Economic Statistics · 1,363
Monte Carlo sampling of solutions to inverse problems
1995 · Klaus Mosegaard, Albert Tarantola · Journal of Geophysical Research Atmospheres · 1,352
Automated docking of substrates to proteins by simulated annealing
1990 · David S. Goodsell, Arthur J. Olson · Proteins Structure Function and Bioinformatics · 1,285
Markov Chain Monte Carlo Maximum Likelihood
1991 · Charles J. Geyer · University of Minnesota Digital Conservancy (University of Minnesota) · 1,077
Probabilistic Inference Using Markov Chain Monte Carlo Methods
2011 · Radford M. Neal · 1,037
Bayesian Computation and Stochastic Systems
1995 · Julian Besag, Peter Green, et al. · Statistical Science · 951
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Constrained Monte Carlo Maximum Likelihood for Dependent Data
1992 · Charles J. Geyer, Elizabeth A. Thompson · Journal of the Royal Statistical Society Series B (Statistical Methodology) · 875
Efficient Metropolis Jumping Rules
1996 · A Gelman*, G O Roberts**, et al. · 797
Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm
1998 · George Kuczera, Eric Parent · Journal of Hydrology · 778
Monte Carlo Simulations
2002 · Daan Frenkel, Berend Smit · Elsevier eBooks · 623
Bayesian Analysis of Stochastic Volatility Models
1994 · Eric Jacquier, Nicholas G. Polson, et al. · Journal of Business and Economic Statistics · 611
MCMCpack : Markov Chain Monte Carlo in R
2011 · Andrew D. Martin, Kevin M. Quinn, et al. · Journal of Statistical Software · 607
Introduction to Monte Carlo Methods
1998 · D. J. C. Mackay · Springer eBooks · 588
A new adaptive importance sampling scheme for reliability calculations
1999 · S.K. Au, J.L. Beck · Structural Safety · 564
Rates of convergence of the Hastings and Metropolis algorithms
1996 · K. L. Mengersen, R. L. Tweedie · The Annals of Statistics · 553
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Works count: 2,034
Citation count: 92,690
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