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Abstract: We propose a new class of learning algorithms that combines variational approximation and Markov chain Monte Carlo (MCMC) simulation. (Update)
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BibTeX entry: (Update)
@inproceedings{ defreitasvariational,
author = "Nando de Freitas and Pedro {H{\o}jen-S{\o}rensen} and Stuart Russell",
title = "Variational {MCMC}",
pages = "120--127",
url = "citeseer.ist.psu.edu/756933.html" }
Citations (may not include all citations):461 Markov Chain Monte Carlo in Practice (context) - Gilks, Richardson et al. - 1996
245 An introduction to variational methods for graphical models - Jordan, Ghahramani et al. - 1999
146 Monte Carlo Statistical Methods - Robert, Casella - 1999
107 Bayesian inference in econometric models using Monte Carlo i.. (context) - Geweke - 1989
58 Rates of convergence of the Hastings and Metropolis algorith.. - Mengersen, Tweedie - 1996
44 Variational inference for Bayesian mixtures of factor analys.. - Ghahramani, Beal - 2000
31 Introduction to Modern Statistical Mechanics (context) - Chandler - 1987
23 Sampling from multimodal distributions using tempered transi.. - Neal - 1996
19 Bayesian parameter estimation via variational methods - Jaakkola, Jordan - 2000
10 Joint Bayesian detection and estimation of noisy sinusoids v.. (context) - Andrieu, Doucet - 1999
10 Variational methods and the QMR-DT database - Jaakkola, Jordan - 1999
9 Robust full Bayesian learning for radial basis networks - Andrieu, de Freitas et al. - 2000
2 and Sahu (context) - Gilks, Roberts - 1998
2 Unsupervised learning from dyadic data (context) - chain, through et al. - 1998
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