0431085 20240103204541.920140912235959.9 84910615503 000363896100141 8 s. C cav_un_epca*0431084978-84-9012-355-3Salamanca, ŠpanělskoInternational Society of Information Fusion2014 distributed estimation big dynamic data bayesian inference cav_un_auth*0242543 Dedecius Kamil UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0263972 Sečkárová Vladimíra UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2014/AS/dedecius-0431085.pdf cav_un_auth*0303543 GP14-06678P GA ČR CZ cav_un_auth*0292725 GA13-13502S GA ČR The big data, characterized by high volume, velocity and variety, often arise in a dynamic way, requiring fast online processing. This contribution proposes a new information-theoretic method for parallel dynamic statistical modelling of such data with a network of cooperating processing units and an optional fusion center. The concept strongly exploits the principles of the Bayesian information processing, allowing its abstract formulation for arbitrary distributions. As a particular case, we specialize to the popular exponential family posterior distributions, arising either directly or indirectly from modelling with generalized linear models. Still, the applicability is considerably wider. cav_un_auth*0305592 17th International Conference on Information Fusion (Fusion 2014) 07.07.2014-10.07.2014 Salamanca ES BB 10000 10100 10103 2015 2 PR http://hdl.handle.net/11104/0236066 1 S RIV/67985556:_____/14:00431085!RIV15-GA0-67985556 152500622 Doplnění UT WOS a Scopus 2014 84910615503 SCOPUS 000363896100141 WOS cav_un_epca*0431084 Proceedings of the 17th International Conference on Information Fusion (Fusion 2014) 978-84-9012-355-3 Salamanca, Španělsko International Society of Information Fusion 2014