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Markov chain Monte Carlo (MCMC) has transformed Bayesian model inference over the past three decades: mainly because of this, Bayesian inference is now a workhorse of applied scientists.
Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
Geman, S. and D. Geman (1984) · 1984
Earlier work this paper cites.
No free lunch theorems for optimization
Wolpert, D. and W. Macready (1997) · 1997
Earlier work this paper cites.
General methods for monitoring convergence of iterative simulations
Brooks, S. P. and A. Gelman (1998) · 1998
Earlier work this paper cites.
Comparative hybridization of an array of 21,500 ovarian cDNAs for the discovery of genes overexpressed in ovarian carcinomas
Schummer, M., W. Ng, R. Bumgarner, P. Nelson, B. Schummer, D. Bednarski, L. Hassell, R. Baldwin, B. Karlan, and L. Hood (1999) · 1999
Earlier work this paper cites.
Winbugs-a Bayesian modelling framework: concepts, structure, and extensibility
Lunn, D., A. Thomas, N. Best, and D. Spiegelhalter (2000) · 2000
Earlier work this paper cites.
Random forests
Breiman, L. (2001) · 2001
Earlier work this paper cites.
Greedy function approximation: a gradient boosting machine
Friedman, J. (2001) · 2001
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Generating random correlation matrices based on vines and extended onion method
Lewandowski, D., D. Kurowicka, and H. Joe (2009) · 2001
Earlier work this paper cites.
The art of data augmentation
Van Dyk, D. and X. Meng (2001) · 2001
Earlier work this paper cites.
Classification and regression by randomforest
Liaw, A. and M. Wiener (2002) · 2002
Earlier work this paper cites.
Gene expression correlates of clinical prostate cancer behavior
Singh, D., P. Febbo, K. Ross, D. Jackson, J. Manola, C. Ladd, P. Tamayo, A. Renshaw, A. D’Amico, and J. Richie (2002) · 2002
Earlier work this paper cites.
Jags: A program for analysis of Bayesian graphical models using Gibbs sampling
Plummer, M. et al. (2003) · 2003
Earlier work this paper cites.
kernlab-an s4 package for kernel methods in r
Karatzoglou, A., A. Smola, K. Hornik, and A. Zeileis (2004) · 2004
Cited alongside, same era.
A stable gene selection in microarray data analysis
Yang, K., Z. Cai, J. Li, and G. Lin (2006) · 2006
Cited alongside, same era.
Building predictive models in R using the Caret package
Kuhn, M. et al. (2008) · 2008
Cited alongside, same era.
Influence of hyperparameters on random forest accuracy
Bernard, S., L. Heutte, and S. Adam (2009) · 2009
Cited alongside, same era.
Expectation propagation for microarray data classification
Hernández-Lobato, D., J. Hernández-Lobato, and A. Suárez (2010) · 2010
Cited alongside, same era.
Handbook of Markov chain Monte Carlo
Brooks, S., A. Gelman, G. Jones, and X. Meng (2011) · 2011
Probabilistic programming in Python using PyMC3
Salvatier, J., T. Wiecki, and C. Fonnesbeck (2016) · 2016
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A conceptual introduction to Hamiltonian Monte Carlo
Betancourt, M. (2017) · 2017
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Stan: A probabilistic programming language
Carpenter, B., A. Gelman, M. Hoffman, D. Lee, B. Goodrich, M. Betancourt, M. Brubaker, J. Guo, P. Li, and A. Riddell (2017) · 2017
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Dillon, J., I. Langmore, D. Tran, E. Brevdo, S. Vasudevan, D. Moore, B. Patton, A. Alemi, M. Hoffman, and R. Saurous (2017) · 2017
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Sparsity information and regularization in the horseshoe and other shrinkage priors
Piironen, J. and A. Vehtari (2017) · 2017
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Cited alongside, same era.
MCMC using Hamiltonian dynamics
Neal, R. et al. (2011) · 2011
Cited alongside, same era.
A probabilistic theory of pattern recognition
Devroye, L., L. Györfi, and G. Lugosi (2013) · 2013
Cited alongside, same era.
Bayesian data analysis
Gelman, A., H. Stern, J. Carlin, D. Dunson, A. Vehtari, and D. Rubin (2013) · 2013
Cited alongside, same era.
The No-U-turn Sampler: adaptively setting path lengths in Hamiltonian Monte Carlo
Hoffman, M. and A. Gelman (2014) · 2014
Cited alongside, same era.
Understanding random forests: From theory to practice
Louppe, G. (2014) · 2014
Cited alongside, same era.
Package ‘nnet’
Ripley, B., W. Venables, and B. Ripley (2016) · 2016
Cited alongside, same era.
Turing: A language for flexible probabilistic inference
Ge, H., K. Xu, and Z. Ghahramani (2018) · 2018
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Pyro: Deep universal probabilistic programming
Bingham, E., J. Chen, M. Jankowiak, F. Obermeyer, N. Pradhan, T. Karaletsos, R. Singh, P. Szerlip, P. Horsfall, and N. Goodman (2019) · 2019
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Hands-on machine learning with R
Boehmke, B. and B. Greenwell (2019) · 2019
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goftest: Classical Goodness-of-Fit Tests for Univariate Distributions
Faraway, J., G. Marsaglia, J. Marsaglia, and A. Baddeley (2019) · 2019
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Package ‘gbm’
Greenwell, B., B. Boehmke, J. Cunningham, G. Developers, and M. B. Greenwell (2019) · 2019
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Implicitly adaptive importance sampling
Paananen, T., J. Piironen, P. Bürkner, and A. Vehtari (2019) · 2019
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Rank-normalization, folding, and localization: An improved r-hat for assessing convergence of MCMC
Vehtari, A., A. Gelman, D. Simpson, B. Carpenter, and P. Bürkner (2020) · 2020
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