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
@Misc{Lawrence:structure01, title = {The Structure of Neural Network Posteriors}, author = {Neil D. Lawrence and Mehdi Azzouzi}, year = {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.} }
Endnote
%0 Generic %T The Structure of Neural Network Posteriors %A Neil D. Lawrence %A Mehdi Azzouzi %D 2001 %F Lawrence:structure01 %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 - GEN TI - The Structure of Neural Network Posteriors AU - Neil D. Lawrence AU - Mehdi Azzouzi DA - 2001/01/01 ID - Lawrence:structure01 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.. (2001). The Structure of Neural Network Posteriors.

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