0463052 20240103212639.420160926235959.9 84978967308 000383292500035 10.1016/j.ins.2016.07.035 16 s. P cav_un_epca*02567520020-02553691 (2016)532-547Elsevier Fully probabilistic design Ideal distribution Minimum cross-entropy principle Bayesian conditioning Kullback-Leibler divergence Bayesian nonparametric modelling cav_un_auth*0213041 34 Quinn A. IR cav_un_auth*0101124 Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems 33 Kárný Miroslav UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101092 Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems 33 Guy Tatiana Valentine UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2016/AS/karny-0463052.pdf cav_un_auth*0292725 GA13-13502S GA ČR The minimum cross-entropy principle is an established technique for design of an un- known distribution, processing linear functional constraints on the distribution. More generally, fully probabilistic design (FPD) chooses the distribution-within the knowledge-constrained set of possible distributions-for which the Kullback-Leibler divergence to the designer’s ideal distribution is minimized. These principles treat the unknown distribution deterministically. In this paper, fully probabilistic design is applied to hierarchical Bayesian models for the first time, yielding optimal design of a (possibly nonparametric) stochastic model for the unknown distribution. This equips minimum cross-entropy and FPD distributional estimates with measures of uncertainty. It enables robust choice of the optimal model, as well as randomization of this choice. The ability to process non-linear functional constraints in the constructed distribution significantly extends the applicability of these principles. BB 2017 3 4 A hod 4ah 20231122141902.5 RVO:67985556 http://hdl.handle.net/11104/0262369 cav_un_auth*0333787 Trinity College Dublin IE 1 Department of Adaptive Systems UTIA-B 10201 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE S 2 Article Computer Science Information Systems COMPUTERSCIENCE.INFORMATIONSYSTEMS 4.732 1.041 4.5 0.04473 1.09 23222 1.781 Q1 3.784 801 Q1 80.37 95.5 Q1 Q1 95.548 2016 Soubory v repozitáři: karny-0463052.pdf 84978967308 SCOPUS 000383292500035 WOS cav_un_epca*0256752 Information Sciences 0020-0255 1872-6291 Roč. 369 č. 1 2016 532 547 Elsevier