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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v-lawrence-structure01, title = {The Structure of Neural Network Posteriors}, author = {Neil D. Lawrence and Mehdi Azzouzi}, year = {}, editor = {}, 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.} }
Endnote
%0 Conference Paper %T The Structure of Neural Network Posteriors %A Neil D. Lawrence %A Mehdi Azzouzi %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-lawrence-structure01 %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 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.
RIS
TY - CPAPER TI - The Structure of Neural Network Posteriors AU - Neil D. Lawrence AU - Mehdi Azzouzi BT - PY - DA - ED - ID - pmlr-v-lawrence-structure01 PB - PMLR SP - DP - PMLR EP - L1 - UR - http://inverseprobability.com/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. ER -
APA
Lawrence, N.D. & Azzouzi, M.. (). The Structure of Neural Network Posteriors. , in PMLR :-

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