0378658 20240103201024.420120828235959.9 10.3182/20120711-3-BE-2027.00104 5 s. P cav_un_epca*0379871978-3-902823-06-91803-1807BrusselsIFAC2012 recursive parameter estimation bounded noise Bayesian learning autoregressive models cav_un_auth*0101175 Pavelková Lenka Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101124 Kárný Miroslav Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2012/AS/pavelkova-approximate bayesian recursive estimation of linear model with uniform noise.pdf TA01030123 GA TA ČR cav_un_auth*0271776 Recursive estimation forms core of adaptive prediction and control. Dynamic exponential family is the only but narrow class of parametric models that allows exact Bayesian estimation. The paper provides an approximate estimation of important autoregressive model with exogenous variables (ARX) and uniform noise. This model reflects well physical nature of modelled system: majority of signals, noise and estimated parameters are bounded. Unlike former solutions, the paper proposes an algorithm that provides a full (approximate) posterior probability density function (pdf) of unknown parameters. Behaviour of the designed algorithm is illustrated by simulations. cav_un_auth*0282287 16th IFAC Symposium on System Identification The International Federation of Automatic Control Brussels 11.07.2012-13.07.2012 BE 2013 BC 2 PR RVO:67985556 http://hdl.handle.net/11104/0210073 2012 cav_un_epca*0379871 Proceedings of the 16th IFAC Symposium on System Identification 978-3-902823-06-9 1803 1807 Brussels IFAC 2012