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Bayesian inference for linear dynamic models with Dirichlet process mixtures

Abstract:

Using Kalman techniques, it is possible to perform optimal estimation in linear Gaussian state-space models. Here, we address the case where the noise probability density functions are of unknown functional form. A flexible Bayesian nonparametric noise model based on Dirichlet process mixtures is introduced. Efficient Markov chain Monte Carlo and sequential Monte Carlo methods are then developed to perform optimal batch and sequential estimation in such contexts. The algorithms are applied to...

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Publication status:
Published

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Publisher copy:
10.1109/TSP.2007.900167

Authors


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Institution:
University of Oxford
Department:
Oxford, MPLS, Statistics
Vanheeghe, P More by this author
Journal:
IEEE TRANSACTIONS ON SIGNAL PROCESSING
Volume:
56
Issue:
1
Pages:
71-84
Publication date:
2008-01-05
DOI:
EISSN:
1941-0476
ISSN:
1053-587X
URN:
uuid:ba0d93d1-858c-444e-8183-f694023cff91
Source identifiers:
172684
Local pid:
pubs:172684

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