research-article

Bayesian Inference for Linear Dynamic Models With Dirichlet Process Mixtures

Authors:

F. Caron,

M. Davy,

A. Doucet,

E. Duflos,

P. VanheegheAuthors Info & Claims

IEEE Transactions on Signal Processing, Volume 56, Issue 1

Pages 71 - 84

https://doi.org/10.1109/TSP.2007.900167

Published: 01 January 2008 Publication History

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 blind deconvolution and change point detection. Experimental results on synthetic and real data demonstrate the efficiency of this approach in various contexts.

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Tang NLiang FJiang D(2024)Semiparametric Bayesian approach to assess non-inferiority with assay sensitivity in a three-arm trial with normally distributed endpointsComputational Statistics10.1007/s00180-023-01384-y39:4(2157-2181)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s00180-023-01384-y
Wang S(2023)Distributionally Robust State Estimation for Jump Linear SystemsIEEE Transactions on Signal Processing10.1109/TSP.2023.332280271(3835-3851)Online publication date: 1-Jan-2023
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Li HMedina DVilà-Valls JClosas P(2021)Robust Variational-Based Kalman Filter for Outlier Rejection With Correlated MeasurementsIEEE Transactions on Signal Processing10.1109/TSP.2020.304294469(357-369)Online publication date: 1-Jan-2021
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Index Terms

Bayesian Inference for Linear Dynamic Models With Dirichlet Process Mixtures
1. Mathematics of computing
  1. Probability and statistics
    1. Probabilistic algorithms
    2. Probabilistic reasoning algorithms
      1. Markov-chain Monte Carlo methods
      2. Sequential Monte Carlo methods

Index terms have been assigned to the content through auto-classification.

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cover image IEEE Transactions on Signal Processing

IEEE Transactions on Signal Processing Volume 56, Issue 1

January 2008

434 pages

ISSN:1053-587X

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IEEE Press

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Published: 01 January 2008

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Tang NLiang FJiang D(2024)Semiparametric Bayesian approach to assess non-inferiority with assay sensitivity in a three-arm trial with normally distributed endpointsComputational Statistics10.1007/s00180-023-01384-y39:4(2157-2181)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s00180-023-01384-y
Wang S(2023)Distributionally Robust State Estimation for Jump Linear SystemsIEEE Transactions on Signal Processing10.1109/TSP.2023.332280271(3835-3851)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TSP.2023.3322802
Li HMedina DVilà-Valls JClosas P(2021)Robust Variational-Based Kalman Filter for Outlier Rejection With Correlated MeasurementsIEEE Transactions on Signal Processing10.1109/TSP.2020.304294469(357-369)Online publication date: 1-Jan-2021
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