The Structure of Neural Network Posteriors

Neil D. Lawrence; Mehdi Azzouzi

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The Structure of Neural Network Posteriors

Neil D. Lawrence, Mehdi Azzouzi

, 2001.

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 explore the structure of the posterior distributions in such a model through a new approximating distribution based on mixtures of Gaussian distributions and show how it may be implemented.

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BibTeX


@Misc{Lawrence:structure01,
  title = 	 {The Structure of Neural Network Posteriors},
  author = 	 {Lawrence, Neil D. and Azzouzi, Mehdi},
  year = 	 {2001},
  pdf = 	 {http://www.thelawrences.net/neil/mixture.pdf},
  url = 	 {/publications/lawrence-structure01.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 explore the structure of the posterior distributions in such a model through a new approximating distribution based on *mixtures* of Gaussian distributions and show how it may be implemented.},
  note =         {}
}

Endnote

%0 Generic
%T The Structure of Neural Network Posteriors
%A Neil D. Lawrence
%A Mehdi Azzouzi
%D 2001	
%F Lawrence:structure01
%U /publications/lawrence-structure01.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 explore the structure of the posterior distributions in such a model through a new approximating distribution based on *mixtures* of Gaussian distributions and show how it may be implemented.
%Z

RIS


TY  - GEN
TI  - The Structure of Neural Network Posteriors
AU  - Neil D. Lawrence
AU  - Mehdi Azzouzi
DA  - 2001/01/01	
ID  - Lawrence:structure01
L1  - http://www.thelawrences.net/neil/mixture.pdf
UR  - /publications/lawrence-structure01.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 explore the structure of the posterior distributions in such a model through a new approximating distribution based on *mixtures* of Gaussian distributions and show how it may be implemented.
N1  - 
ER  -

APA


Lawrence, N.D. & Azzouzi, M.. (2001). The Structure of Neural Network Posteriors.  Available from /publications/lawrence-structure01.html.