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Gibbs sampling is a workhorse for Bayesian inference but has several limitations when used for parameter estimation, and is often much slower than non-sampling inference methods.
Maximum likelihood from incomplete data via the em algorithm
A. P. Dempster, N. M. Laird, D. B. Rubin, et al · 1977
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Stochastic relaxation, gibbs distributions, and the bayesian restoration of images
S. Geman and D. Geman · 1984
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A monte carlo implementation of the em algorithm and the poor man’s data augmentation algorithms
G. C. Wei and M. A. Tanner · 1990
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An introduction to variational methods for graphical models
M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul · 1999
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Marginal MAP estimation using markov chain monte-carlo
C. Robert, A. Doucet, and S. Godsill · 1999
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Correctness of local probability propagation in graphical models with loops
Y. Weiss · 2000
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Expectation propagation for approximate bayesian inference
T. Minka · 2001
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Marginal maximum a posteriori estimation using markov chain monte carlo
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Latent dirichlet allocation
D. M. Blei, A. Y. Ng, and M. I. Jordan · 2003
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F. Yan, N. Xu, and Y. Qi · 2009
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Large-scale machine learning with stochastic gradient descent
L. Bottou · 2010
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Online learning for latent dirichlet allocation
M. D. Hoffman, D. M. Blei, and F. R. Bach · 2010
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Scalable inference in latent variable models
A. Ahmed, M. Aly, J. Gonzalez, S. Narayanamurthy, and A. J. Smola · 2012
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Bidmach: Large-scale learning with zero memory allocation
J. Canny and H. Zhao · 2013
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Big data analytics with small footprint: Squaring the cloud
J. Canny and H. Zhao · 2013
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