bibtype	C - Conference Paper (international conference)
ARLID	0479432
utime	20240103214658.9
mtime	20171012235959.9
title (primary) (eng)	Performance of Kullback-Leibler Based Expert Opinion Pooling for Unlikely Events
specification	page_count 10 s. media_type E
serial	ARLID cav_un_epca*0479516 ISSN Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers title Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers page_num 41-50 publisher place Cambridge name JMLR year 2017 editor name1 Guy name2 Tatiana Valentine editor name1 Kárný name2 Miroslav editor name1 Rios-Insua name2 D. editor name1 Wolpert name2 D. H.
keyword	Opinion Pooling
keyword	Combining Probability Distributions
keyword	Minimum KullbackLeibler Divergence
author (primary)	ARLID cav_un_auth*0263972 full_dept (cz) Adaptivní systémy full_dept (eng) Department of Adaptive Systems department (cz) AS department (eng) AS full_dept Department of Adaptive Systems share 100 name1 Sečkárová name2 Vladimíra institution UTIA-B country CZ fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source	url http://library.utia.cas.cz/separaty/2017/AS/seckarova-0479432.pdf
cas_special	project ARLID cav_un_auth*0331019 project_id GA16-09848S agency GA ČR country CZ abstract (eng) The aggregation of available information is of great importance in many branches of economics,\nsocial sciences. Often, we can only rely on experts’ opinions, i.e. probabilities assigned to possible events. To deal with opinions in probabilistic form, we focus on the Kullback-Leibler (KL) divergence based pools: linear, logarithmic and KL-pool (Seckarova, 2015). Since occurrence of events is subject to random influences of the real world, it is important to address events assigned lower probabilities (unlikely events). This is done by choosing pooling with a higher entropy than standard linear or logarithmic options, i.e. the KL-pool. We show how well the mentioned pools perform on real data using absolute error, KL-divergence and quadratic reward. In cases favoring events assigned higher probabilities, the KL-pool performs similarly to the linear pool and outperforms the logarithmic pool. When unlikely events occur, the KL-pool outperforms both pools, which makes it a reasonable way of pooling.\n action ARLID cav_un_auth*0351361 name NIPS 2016 Workshop on Imperfect Decision Makers dates 20161209 mrcbC20-s 20161209 place Barcelona country ES RIV BC FORD0 10000 FORD1 10100 FORD2 10103 reportyear 2018 num_of_auth 1 presentation_type PR inst_support RVO:67985556 permalink http://hdl.handle.net/11104/0275502 confidential S arlyear 2017 mrcbU14 SCOPUS mrcbU24 PUBMED mrcbU34 WOS mrcbU63 cav_un_epca*0479516 Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers 1938-7228 41 50 Cambridge JMLR 2017 Proceedings of Machine Learning Research volume 58 mrcbU67 Guy Tatiana Valentine 340 mrcbU67 340 Kárný Miroslav mrcbU67 340 Rios-Insua D. mrcbU67 340 Wolpert D. H.