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Gibbs sampling is the de facto Markov chain Monte Carlo method used for inference and learning on large scale graphical models.
Beitrag zur theorie des ferromagnetismus
Ernst Ising · 1925
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Some generalized order-disorder transformations
Renfrey Burnard Potts · 1952
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Monte Carlo sampling methods using Markov chains and their applications
W Keith Hastings · 1970
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Distributed inference for latent dirichlet allocation
David Newman, Padhraic Smyth, Max Welling, and Arthur U Asuncion · 2007
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Probabilistic graphical models: principles and techniques
Daphne Koller and Nir Friedman · 2009
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Markov chains and mixing times
David Asher Levin, Yuval Peres, and Elizabeth Lee Wilmer · 2009
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The BUGS project: evolution, critique and future directions
David Lunn, David Spiegelhalter, Andrew Thomas, and Nicky Best · 2009
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Training sparse natural image models with a fast Gibbs sampler of an extended state space
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Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach
Rémi Bardenet, Arnaud Doucet, and Chris Holmes · 2014
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Anoop Korattikara, Yutian Chen, and Max Welling · 2014
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Firefly Monte Carlo: exact MCMC with subsets of data
Dougal Maclaurin and Ryan P Adams · 2014
Approximations of Markov chains and Bayesian inference
James E Johndrow, Jonathan C Mattingly, Sayan Mukherjee, and David Dunson · 2015
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Ensuring rapid mixing and low bias for asynchronous Gibbs sampling
Christopher De Sa, Christopher Ré, and Kunle Olukotun · 2016
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Scan order in Gibbs sampling: Models in which it matters and bounds on how much
Bryan He, Christopher De Sa, Ioannis Mitliagkas, and Christopher Ré · 2016
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Dangna Li and Wing H Wong · 2017
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Holoclean: Holistic data repairs with probabilistic inference
Theodoros Rekatsinas, Xu Chu, Ihab F Ilyas, and Christopher Ré · 2017
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DimmWitted: A study of main-memory statistical analytics
Ce Zhang and Christopher Ré · 2014
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Daniel Seita, Xinlei Pan, Haoyu Chen, and John F. Canny · 2017
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The block-Poisson estimator for optimally tuned exact subsampling MCMC
M. Quiroz, M.-N. Tran, M. Villani, R. Kohn, and K.-D. Dang · 2018
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