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The article addresses a long-standing open problem on the justification of using variational Bayes methods for parameter estimation.
On Bayes procedures
Lorraine Schwartz · 1965
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Convergence of estimates under dimensionality restrictions
Lucien LeCam · 1973
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Approximation dans les espaces métriques et théorie de l’estimation
Lucien Birgé · 1983
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Sampling-based approaches to calculating marginal densities
Alan E Gelfand and Adrian FM Smith · 1990
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Optimal rate of convergence for finite mixture models
Jiahua Chen · 1995
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Stochastic approximation algorithms and applications
Harold J Kushner and G George Yin · 1997
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An introduction to variational methods for graphical models
Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul · 1999
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A variational Bayesian framework for graphical models
Hagai Attias · 2000
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Convergence rates of posterior distributions
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K Humphreys and DM Titterington · 2000
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Matthew Stephens · 2000
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Aad W Van der Vaart · 2000
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Variational Bayesian model selection for mixture distributions
Adrian Corduneanu and Christopher M Bishop · 2001
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Propagation algorithms for variational bayesian learning
Zoubin Ghahramani and Matthew J Beal · 2001
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Entropies and rates of convergence for maximum likelihood and bayes estimation for mixtures of normal densities
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Messan Amewou-Atisso, Subhashis Ghosal, Jayanta K Ghosh, and RV Ramamoorthi · 2003
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David M Blei, Andrew Y Ng, and Michael I Jordan · 2003
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Posterior contraction in sparse bayesian factor models for massive covariance matrices
Debdeep Pati, Anirban Bhattacharya, Natesh S Pillai, and David Dunson · 2014
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Minimax optimal Bayesian aggregation
Yun Yang and David B Dunson · 2014
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Rate-optimal posterior contraction for sparse pca
Chao Gao, Harrison H Zhou, et al · 2015
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Optimal Bayesian estimation in random covariate design with a rescaled Gaussian process prior
Debdeep Pati, Anirban Bhattacharya, and Guang Cheng · 2015
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