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Towards a Supra-Bayesian Approach to Merging of Information
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SYSNO ASEP 0368326 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Towards a Supra-Bayesian Approach to Merging of Information Author(s) Sečkárová, Vladimíra (UTIA-B) RID Number of authors 1 Source Title The 2nd International Workshop od Decision Making with Multiple Imperfect Decision Makers. Held in Conjunction with the 25th Annual Conference on Neural Information Processing Systems (NIPS 2011). - Prague : Institute of Information Theory and Automation, 2011 - ISBN 978-80-903834-6-3 Pages s. 81-86 Number of pages 6 s. Action The 2nd International Workshop od Decision Making with Multiple Imperfect Decision Makers. Held in Conjunction with the 25th Annual Conference on Neural Information Processing Systems (NIPS 2011) Event date 16.12.2011-16.12.2011 VEvent location Sierra Nevada Country ES - Spain Event type WRD Language eng - English Country CZ - Czech Republic Keywords decision makers ; Supra-Bayesian ; Bayesian solution ; Merging Subject RIV BB - Applied Statistics, Operational Research R&D Projects 1M0572 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) GA102/08/0567 GA ČR - Czech Science Foundation (CSF) CEZ AV0Z10750506 - UTIA-B (2005-2011) Annotation Merging of information given by different decision makers (DMs) has become a much discussed topic in recent years and many procedures were developed towards it. The main and the most discussed problem is the incompleteness of given information. Little attention is paid to the possible forms in which the DMs provide them; in most of cases arising procedures are working only for a particular type of information. Recently introduced Supra-Bayesian approach to merging of information brings a solution to two previously mentioned problems. All is based on a simple idea of unifying all given information into one form and treating the possible incompleteness. In this article, beside a brief repetition of the method, we show, that the constructed merger of information reduces to the Bayesian solution if information calls for this. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2012
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