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Variational Bayes for generalized autoregressive models

Abstract:

We describe a variational Bayes (VB) learning algorithm for generalized autoregressive (GAR) models. The noise is modeled as a mixture of Gaussians rather than the usual single Gaussian. This allows different data points to be associated with different noise levels and effectively provides robust estimation of AR coefficients. The VB framework is used to prevent overfitting and provides model-order selection criteria both for AR order and noise model order. We show that for the special case o...

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Publication status:
Published

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Publisher copy:
10.1109/TSP.2002.801921

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Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
Journal:
IEEE TRANSACTIONS ON SIGNAL PROCESSING
Volume:
50
Issue:
9
Pages:
2245-2257
Publication date:
2002-09-05
DOI:
ISSN:
1053-587X
URN:
uuid:914e5acc-3133-42cb-bd33-dacd56d8d94f
Source identifiers:
63138
Local pid:
pubs:63138

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