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Markov chain Monte Carlo is a key computational tool in Bayesian statistics, but it can be challenging to monitor the convergence of an iterative stochastic algorithm.
The use of ranks to avoid the assumption of normality implicit in the analysis of variance
Milton Friedman · 1937
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Statistical Tables for Biological, Agricultural, and Medical Research
Ronald A. Fisher and Frank Yates · 1938
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Statistical Estimates and Transformed Beta-Variables
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Asymptotic normality and efficiency of certain nonparametric test statistics
Herman Chernoff and I. Richard Savage · 1958
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Monte Carlo sampling methods using Markov chains and their applications
W. K. Hastings · 1970
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Statistical considerations in the evaluation of climatic experiments with atmospheric general circulation models
John A. Laurmann and W. Lawrence Gates · 1977
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Andrew Gelman and Donald B. Rubin · 1992
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Charles J. Geyer · 1992
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How many iterations in the Gibbs sampler?
Adrian E. Raftery and Steven M. Lewis · 1992
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Augustine Kong, Jun S. Liu, and Wing Hung Wong · 1994
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Bayesian inference in threshold models using Gibbs sampling
D. A. Sorensen, S. Andersen, D. Gianola, and I. Korsgaard · 1995
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Markov chain Monte Carlo convergence diagnostics: A comparative review
Mary Kathryn Cowles and Bradley P. Carlin · 1996
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New extension of the kalman filter to nonlinear systems
Simon J Julier and Jeffrey K Uhlmann · 1997
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MCMC convergence diagnostics: A review
Kerrie L. Mengersen, Christian P. Robert, and Chantal Guihenneuc-Jouyaux · 1999
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David J Lunn, Andrew Thomas, Nicky Best, and David Spiegelhalter · 2000
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Bayesian Data Analysis, second edition
Andrew Gelman, John B. Carlin, Hal S. Stern, and Donald R. Rubin · 2003
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Radford M. Neal · 2003
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Markov chain Monte Carlo estimation of quantiles
Charles R. Doss, James M. Flegal, Galin L. Jones, and Ronald C. Neath · 2014
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The No-U-Turn Sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo
Matthew D. Hoffman and Andrew Gelman · 2014
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The number of MCMC draws needed to compute Bayesian credible bounds
Jia Liu, Daniel J. Nordman, and William Q. Meeker · 2016
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John Salvatier, Thomas V. Wiecki, and Christopher Fonnesbeck · 2016
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Hamiltonian Monte Carlo for hierarchical models
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