0477043 20240103214400.020170815235959.9 85020024365 000418403500007 10.1007/978-3-319-54084-9_7 11 s. P cav_un_epca*0474860978-3-319-54083-22194-100967-77ChamSpringer International Publishing2017ArgientoR.LanzaroneE.VillalobosI. A.MatteiA. Model averaging Model uncertainty Prediction Tempered likelihood cav_un_auth*0306030 Reichl Jan Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0242543 Dedecius Kamil Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems CZ Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2017/AS/dedecius-0477043.pdf cav_un_auth*0303543 GP14-06678P GA ČR CZ We study the problem of online prediction with a set of candidate models using dynamic model averaging procedures. The standard assumptions of model averaging state that the set of admissible models contains the true one(s), and that these models are continuously updated by valid data. However, both these assumptions are often violated in practice. The models used for online tasks are often more or less misspecified and the data corrupted (which is, mathematically, a demonstration of the same problem). Both these factors negatively influence the Bayesian inference and the resulting predictions. In this paper, we propose to suppress these issues by extending the Bayesian update by a sort of likelihood tempering, moderating the impact of observed data to inference. The method is compared to the generic dynamic model averaging and to an alternative solution via sequential quasi-Bayesian mixture modeling. cav_un_auth*0346501 Bayesian Young Statisticians Meeting, BAYSM 2016 20160619 20160621 Florence IT BB 10000 10100 10103 2018 2 PO RVO:67985556 http://hdl.handle.net/11104/0274025 S n.a. Proceedings Paper Statistics Probability 3+4 Proceedings Paper Statistics Probability 3+4 Proceedings Paper Statistics Probability 0.217 Q4 2017 85020024365 SCOPUS PUBMED 000418403500007 WOS cav_un_epca*0474860 Bayesian Statistics in Action Springer International Publishing 2017 Cham 67 77 978-3-319-54083-2 2194-1009 340 Argiento R. 340 Lanzarone E. 340 Villalobos I. A. 340 Mattei A.