0352026 20240103194400.520110103235959.9 1 s. cav_un_epca*035302153-53PrahaÚTIA AV ČR, v.v.i2010 robust bayesian auto-regression cav_un_auth*0101205 Šindelář Jan Stochastická informatika Department of Stochastic Informatics SI SI UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2010/SI/sindelar-robust bayesian auto-regression model.pdf 1M0572 GA MŠk cav_un_auth*0001814 GA102/08/0567 GA ČR cav_un_auth*0239566 CEZ:AV0Z10750506 The problem of estimating parameters of an auto-regression model in a Bayesian paradigm has been solved before, when the model has innovations coming from exponential family. The main reason for choosing exponential family was the simplicity of computation and the fact that Gaussian distribution, often found in nature due to existence of limit theorems, is also a member of this family. Applications of modeling to data, where the distribution of innovations is known to be heavy-tailed calls for a method, more robust with respect to possible outliers. We choose the 1-D innovations of the model to be Laplace distributed, choose a Bayesian conjugate prior to such a model distribution and try to compute the resulting filtration, when new data of a realization of an adjacent random process arrive. The computation of the resultant posterior distribution of the parameters of the model is still computationally tractable as will be shown. cav_un_auth*0267155 6th International Workshop on Data–Algorithms–Decision Making Jindřichův Hradec 2.12.2010-4.12.2010 CZ 2011 BB 4 O 4o 20231122134328.4 http://hdl.handle.net/11104/0191635 2010 Soubory v repozitáři: 0352026.pdf cav_un_epca*0353021 Proceedings of Abstracts of the 6th. International Conference on Data - Algorithms - Decision Making 53 53 Praha ÚTIA AV ČR, v.v.i 2010