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Fully probabilistic design of hierarchical Bayesian models
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SYSNO ASEP 0463052 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Fully probabilistic design of hierarchical Bayesian models Author(s) Quinn, A. (IR)
Kárný, Miroslav (UTIA-B) RID, ORCID
Guy, Tatiana Valentine (UTIA-B) RID, ORCIDNumber of authors 3 Source Title Information Sciences. - : Elsevier - ISSN 0020-0255
Roč. 369, č. 1 (2016), s. 532-547Number of pages 16 s. Publication form Print - P Language eng - English Country US - United States Keywords Fully probabilistic design ; Ideal distribution ; Minimum cross-entropy principle ; Bayesian conditioning ; Kullback-Leibler divergence ; Bayesian nonparametric modelling Subject RIV BB - Applied Statistics, Operational Research R&D Projects GA13-13502S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 UT WOS 000383292500035 EID SCOPUS 84978967308 DOI 10.1016/j.ins.2016.07.035 Annotation 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. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2017
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