A Variational Bayesian Committee of Neural Networks

, 1999.

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.

Cite this Paper

BibTeX

@InProceedings{pmlr-v-lawrence-nnmixtures99,
  title = 	 {A Variational Bayesian Committee of Neural Networks},
  author = 	 {Neil D. Lawrence and Mehdi Azzouzi},
  year = 	 {},
  editor = 	 {},
  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.
}
}

Endnote

%0 Conference Paper
%T A Variational Bayesian Committee of Neural Networks
%A Neil D. Lawrence
%A Mehdi Azzouzi
%B 
%C Proceedings of Machine Learning Research	
%D 
%E 	
%F pmlr-v-lawrence-nnmixtures99
%I PMLR	
%J Proceedings of Machine Learning Research	
%P --
%U http://inverseprobability.com
%V 
%W PMLR
%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.

RIS

TY  - CPAPER
TI  - A Variational Bayesian Committee of Neural Networks
AU  - Neil D. Lawrence
AU  - Mehdi Azzouzi
BT  - 
PY  - 
DA  - 
ED  - 	
ID  - pmlr-v-lawrence-nnmixtures99
PB  - PMLR	
SP  - 
DP  - PMLR
EP  - 
L1  - 
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  -

APA

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

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