0393989 20240103202715.320130730235959.9 10 s. P cav_un_epca*03939880204-9805221 (2013)159-168 Kerridge inaccuracy maximum entropy principle parameter estimation cav_un_auth*0263972 Sečkárová Vladimíra 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/2013/AS/seckarova-on supra-bayesian weighted combination of available data determined by kerridge inaccuracy ane entropy.pdf GA13-13502S GA ČR cav_un_auth*0292725 SVV-265315 GA MŠk CZ cav_un_auth*0291444 Every process in our environment can be described with a statistical model containing inner properties expressed by parameters. These are usually unknown and the determination of their values is of interest in the statistical branch called parameter estimation. This branch involves many methods solving different estimation cases, e.g. the estimation of location and scale parameters. To obtain the parameter estimate we exploit the data given by data sources. In particular, the estimate is their combination. Improvement of the parameter estimates involve the assignment of the weights to the data sources resulting in a weighted combination of data. In this paper we focus on the derivation of the weights arisen within the Supra-Bayesian approach and on the simulation study of their behaviour and the behaviour of the final estimate. 2014 BD 1 RVO:67985556 http://hdl.handle.net/11104/0222589 2013 cav_un_epca*0393988 Pliska Studia Mathematica Bulgarica 0204-9805 Roč. 22 č. 1 2013 159 168