Recursive Bayesian estimation of autoregressive model with uniform noise using approximation by parallelotopes
Summary This paper proposes a recursive algorithm for the estimation of a stochastic autoregressive model with an external input. The noise of the involved model is described by a uniform distribution. The model parameters are estimated using the Bayesian approach. Without an approximation, the supp...
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| Published in | International journal of adaptive control and signal processing Vol. 31; no. 8; pp. 1184 - 1192 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Chichester, UK
John Wiley & Sons, Ltd
01.08.2017
Wiley Subscription Services, Inc |
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| Online Access | Get full text |
| ISSN | 0890-6327 1099-1115 |
| DOI | 10.1002/acs.2756 |
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| Abstract | Summary
This paper proposes a recursive algorithm for the estimation of a stochastic autoregressive model with an external input. The noise of the involved model is described by a uniform distribution. The model parameters are estimated using the Bayesian approach. Without an approximation, the support of the posterior distribution is a complex multidimensional polytope whose number of faces increases with time. We propose an approximation of this polytope in each time step by a parallelotope with a constant number of faces. The behaviour of the proposed algorithm is illustrated by simulations and compared with other methods. |
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| AbstractList | Summary
This paper proposes a recursive algorithm for the estimation of a stochastic autoregressive model with an external input. The noise of the involved model is described by a uniform distribution. The model parameters are estimated using the Bayesian approach. Without an approximation, the support of the posterior distribution is a complex multidimensional polytope whose number of faces increases with time. We propose an approximation of this polytope in each time step by a parallelotope with a constant number of faces. The behaviour of the proposed algorithm is illustrated by simulations and compared with other methods. Summary This paper proposes a recursive algorithm for the estimation of a stochastic autoregressive model with an external input. The noise of the involved model is described by a uniform distribution. The model parameters are estimated using the Bayesian approach. Without an approximation, the support of the posterior distribution is a complex multidimensional polytope whose number of faces increases with time. We propose an approximation of this polytope in each time step by a parallelotope with a constant number of faces. The behaviour of the proposed algorithm is illustrated by simulations and compared with other methods. This paper proposes a recursive algorithm for the estimation of a stochastic autoregressive model with an external input. The noise of the involved model is described by a uniform distribution. The model parameters are estimated using the Bayesian approach. Without an approximation, the support of the posterior distribution is a complex multidimensional polytope whose number of faces increases with time. We propose an approximation of this polytope in each time step by a parallelotope with a constant number of faces. The behaviour of the proposed algorithm is illustrated by simulations and compared with other methods. |
| Author | Pavelková, Lenka Jirsa, Ladislav |
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| Cites_doi | 10.1214/aoms/1177729694 10.1109/TAC.1982.1102926 10.1002/acs.4480090111 10.1007/978-3-642-21981-8 10.1016/0005-1098(93)90020-T 10.3182/20120711-3-BE-2027.00104 10.1214/aos/1176344689 10.5220/0005545706030607 10.1016/j.automatica.2004.02.014 10.1016/j.ins.2014.01.048 10.5220/0005044506710677 10.1109/9.506230 10.1016/j.sysconle.2007.03.005 |
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This paper proposes a recursive algorithm for the estimation of a stochastic autoregressive model with an external input. The noise of the involved... This paper proposes a recursive algorithm for the estimation of a stochastic autoregressive model with an external input. The noise of the involved model is... Summary This paper proposes a recursive algorithm for the estimation of a stochastic autoregressive model with an external input. The noise of the involved... |
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| SubjectTerms | approximate parameter estimation Approximation ARX model Autoregressive models Autoregressive processes Bayesian analysis Bayesian estimation bounded noise Computer simulation Kullback‐Leibler divergence parallelotope Parameter estimation Recursive algorithms |
| Title | Recursive Bayesian estimation of autoregressive model with uniform noise using approximation by parallelotopes |
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