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Empirische Inferenz Technical Report 2010

Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference

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Empirische Inferenz
Hannes Nickisch
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Empirische Inferenz
Matthias Seeger

We propose a novel algorithm to solve the expectation propagation relaxation of Bayesian inference for continuous-variable graphical models. In contrast to most previous algorithms, our method is provably convergent. By marrying convergent EP ideas from (Opper&Winther 05) with covariance decoupling techniques (Wipf&Nagarajan 08, Nickisch&Seeger 09), it runs at least an order of magnitude faster than the most commonly used EP solver.

Author(s): Seeger, M. and Nickisch, H.
Year: 2010
Month: December
Day: 0
Bibtex Type: Technical Report (techreport)
Digital: 0
Electronic Archiving: grant_archive
Institution: Max Planck Institute for Biological Cybernetics
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik
Links:

BibTex 

@techreport{6995,
  title = {Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference},
  abstract = {We propose a novel algorithm to solve the expectation propagation relaxation of Bayesian inference for continuous-variable graphical models. In contrast to most previous algorithms, our method is provably convergent. By marrying convergent EP ideas from (Opper&Winther 05) with covariance decoupling techniques (Wipf&Nagarajan 08, Nickisch&Seeger 09), it runs at least an order of magnitude faster than the most commonly used EP solver.},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max Planck Institute for Biological Cybernetics},
  school = {Biologische Kybernetik},
  month = dec,
  year = {2010},
  slug = {6995},
  author = {Seeger, M. and Nickisch, H.},
  month_numeric = {12}
}