The Structure of Neural Network Posteriors

[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 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.


@TechReport{lawrence-structure01,
  title = 	 {The Structure of Neural Network Posteriors},
  author = 	 {Neil D. Lawrence and Mehdi Azzouzi},
  year = 	 {2001},
  month = 	 {00},
  edit = 	 {https://github.com/lawrennd//publications/edit/gh-pages/_posts/2001-01-01-lawrence-structure01.md},
  url =  	 {http://inverseprobability.com/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.},
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AU  - Mehdi Azzouzi
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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.
ER  -

Lawrence, N.D. & Azzouzi, M.. (2001). The Structure of Neural Network Posteriors.:-