A Variational Bayesian Committee of Neural Networks

[edit]

Neil D. Lawrence, University of Sheffield
Mehdi Azzouzi

Related Material

Abstract

Exact inference in Bayesian neural networks is non analytic to compute and as a result approximate approaches such as the evidence procedure, Monte-Carlo sampling and variational inference have been proposed. In this paper we present a general overview of the Bayesian approach with a particular emphasis on the variational procedure. We then present a new approximating distribution based on mixtures of Gaussian distributions and show how it may be implemented. We present results on a simple toy problem and on two real world data-sets.


@TechReport{lawrence-nnmixtures99,
  title = 	 {A Variational Bayesian Committee of Neural Networks},
  author = 	 {Neil D. Lawrence and Mehdi Azzouzi},
  year = 	 {1999},
  month = 	 {00},
  edit = 	 {https://github.com/lawrennd//publications/edit/gh-pages/_posts/1999-01-01-lawrence-nnmixtures99.md},
  url =  	 {http://inverseprobability.com/publications/lawrence-nnmixtures99.html},
  abstract = 	 {Exact inference in Bayesian neural networks is non analytic to compute and as a result approximate approaches such as the evidence procedure, Monte-Carlo sampling and variational inference have been proposed. In this paper we present a general overview of the Bayesian approach with a particular emphasis on the variational procedure. We then present a new approximating distribution based on *mixtures* of Gaussian distributions and show how it may be implemented. We present results on a simple toy problem and on two real world data-sets.},
  key = 	 {Lawrence:nnmixtures99},
  linkpsgz =  {http://www.thelawrences.net/neil/nnmixture.ps.gz},
  OPTgroup = 	 {}
 

}
%T A Variational Bayesian Committee of Neural Networks
%A Neil D. Lawrence and Mehdi Azzouzi
%B 
%D 
%F lawrence-nnmixtures99	
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%R 
%U http://inverseprobability.com/publications/lawrence-nnmixtures99.html
%X Exact inference in Bayesian neural networks is non analytic to compute and as a result approximate approaches such as the evidence procedure, Monte-Carlo sampling and variational inference have been proposed. In this paper we present a general overview of the Bayesian approach with a particular emphasis on the variational procedure. We then present a new approximating distribution based on *mixtures* of Gaussian distributions and show how it may be implemented. We present results on a simple toy problem and on two real world data-sets.
TY  - CPAPER
TI  - A Variational Bayesian Committee of Neural Networks
AU  - Neil D. Lawrence
AU  - Mehdi Azzouzi
PY  - 1999/01/01
DA  - 1999/01/01	
ID  - lawrence-nnmixtures99	
SP  - 
EP  - 
UR  - http://inverseprobability.com/publications/lawrence-nnmixtures99.html
AB  - Exact inference in Bayesian neural networks is non analytic to compute and as a result approximate approaches such as the evidence procedure, Monte-Carlo sampling and variational inference have been proposed. In this paper we present a general overview of the Bayesian approach with a particular emphasis on the variational procedure. We then present a new approximating distribution based on *mixtures* of Gaussian distributions and show how it may be implemented. We present results on a simple toy problem and on two real world data-sets.
ER  -

Lawrence, N.D. & Azzouzi, M.. (1999). A Variational Bayesian Committee of Neural Networks.:-