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Variational Bayesian Inference for a Nonlinear Forward Model

Abstract:

Variational Bayes (VB) has been proposed as a method to facilitate calculations of the posterior distributions for linear models, by providing a fast method for Bayesian inference by estimating the parameters of a factorized approximation to the posterior distribution. Here a VB method for nonlinear forward models with Gaussian additive noise is presented. In the case of noninformative priors the parameter estimates obtained from this VB approach are identical to those found via nonlinear lea...

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Publication status:
Published

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Publisher copy:
10.1109/TSP.2008.2005752

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Psychiatry
Role:
Author
Journal:
IEEE TRANSACTIONS ON SIGNAL PROCESSING
Volume:
57
Issue:
1
Pages:
223-236
Publication date:
2009-01-01
DOI:
EISSN:
1941-0476
ISSN:
1053-587X
Source identifiers:
186271
Language:
English
Keywords:
Pubs id:
pubs:186271
UUID:
uuid:1e31142d-b892-4910-807f-7cf44f290a9b
Local pid:
pubs:186271
Deposit date:
2012-12-19

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