Variational-Bayes .org

  VIBES (Variational Inference for BayESian Networks)

Description: Inference engine for performing variational inference in Bayesian networks.

VIBES: Variational Inference for BayESian Networks

VIBES 2.0 is now available!

Visit http://vibes.sourceforge.net for installation instructions, a tutorial, example files and online help.

  Variational Bayesian Mixtures of Factor Analysers

Description: Performs discrete changes to model structure by birth and death of mixture components, and simultaneously continuously determines each component's latent-space dimensionalities via automatic relevance determination. The relevant citation is

• Ghahramani, Z. and Beal, M.J. (2000)
  Variational Inference for Bayesian Mixtures of Factor Analysers
  In Advances in Neural Information Processing Systems 12:449-455, eds. S. A. Solla, T.K. Leen, K. Müller, MIT Press, 2000.
  [abstract] [pdf] [ps.gz] [poster] [software]

• See also: Beal, M.J., Ghahramani, Z. (2002) The Variational Bayesian EM Algorithm for Incomplete Data: with Application to Scoring Graphical Model Structures [abstract] [pdf] [ps.gz]
• Download, unzip and extract the vbmfa.tar.gz file for vbmfa functions.
• The graphical model implemented is not exactly as in the NIPS paper; the better model looks like this.
• Follow the included readme file for help.

  Variational Bayesian State-Space Models (aka Linear Dynamical Systems)

Description: Implements an approximation to full Bayesian state-space models (aka linear dynamical systems), allowing dimensionality determination of the hidden state via automatic relevance determination. The tar includes the variational Kalman Smoother function, which is called as a subroutine. Kalman Smoother derivation. The relevant NIPS paper is:

• Ghahramani, Z. and Beal, M.J. (2000)
  Propagation Algorithms for Variational Bayesian Learning
  In Advances in Neural Information Processing Systems 13, eds. T.K. Leen, T. Dietterich, V. Tresp, MIT Press, 2001.
  [abstract] [pdf] [ps.gz] [poster] [software]

  Please email me for Matlab code.

  Variational Bayesian Hidden Markov Models

Description: Implementation of vb HMMs with a simple demo on letter strings. The relevant paper for this code is an unpublished report:

• Mackay, D.J.C.
  Ensemble Learning for Hidden Markov Models
  [abstract] [pdf] [ps.gz]

• Thanks to Zoubin Ghahramani and Andy Brown for writing parts of the code.
• Download, unzip and extract the vbhmm.tar.gz file for vbhmm functions (use tar -xvzf vbhmm.tar.gz).
• To get started, type vbhmm_demo at the Matlab prompt.
• or type help vbhmm or help vbhmm_cF.

VB Home

VB Papers

VB Software

VB People

Submit