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Variational message passing

Known as: Message passing (disambiguation), Passing 
Variational message passing (VMP) is an approximate inference technique for continuous- or discrete-valued Bayesian networks, with conjugate… Expand
Wikipedia

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2018
2018
Recent efforts on combining deep models with probabilistic graphical models are promising in providing flexible models that are… Expand
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2018
2018
Bayesian inference plays an important role in advancing machine learning, but faces computational challenges when applied to… Expand
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2018
2018
We propose a novel distributed inference algorithm for continuous graphical models, by extending Stein variational gradient… Expand
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Highly Cited
2016
Highly Cited
2016
Localization and synchronization are very important in many wireless applications such as monitoring and vehicle tracking… Expand
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Highly Cited
2014
Highly Cited
2014
  • M. Wand
  • J. Mach. Learn. Res.
  • 2014
  • Corpus ID: 17990945
Fully simplified expressions for Multivariate Normal updates in non-conjugate variational message passing approximate inference… Expand
Highly Cited
2011
Highly Cited
2011
Variational Message Passing (VMP) is an algorithmic implementation of the Variational Bayes (VB) method which applies only in the… Expand
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Highly Cited
2011
Highly Cited
2011
We propose a novel algorithm for sensor self-localization in cooperative wireless networks where observations of relative sensor… Expand
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2008
2008
As richer models for stereo vision are constructed, there is a growing interest in learning model parameters. To estimate… Expand
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Highly Cited
2007
Highly Cited
2007
  • J. Dauwels
  • IEEE International Symposium on Information…
  • 2007
  • Corpus ID: 15277096
In this paper, it is shown how (naive and structured) variational algorithms may be derived from a factor graph by mechanically… Expand
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Highly Cited
2005
Highly Cited
2005
Bayesian inference is now widely established as one of the principal foundations for machine learning. In practice, exact… Expand
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