bibtype J - Journal Article
ARLID 0367271
utime 20240103195840.3
mtime 20111125235959.9
WOS 000298460300007
SCOPUS 81355127223
DOI 10.1016/j.ins.2011.09.018
title (primary) (eng) Axiomatisation of fully probabilistic design
specification
page_count 9 s.
serial
ARLID cav_un_epca*0256752
ISSN 0020-0255
title Information Sciences
volume_id 186
volume 1 (2012)
page_num 105-113
publisher
name Elsevier
keyword Bayesian decision making
keyword Fully probabilistic design
keyword Kullback–Leibler divergence
keyword Unified decision making
author (primary)
ARLID cav_un_auth*0101124
name1 Kárný
name2 Miroslav
full_dept (cz) Adaptivní systémy
full_dept (eng) Department of Adaptive Systems
department (cz) AS
department (eng) AS
institution UTIA-B
full_dept Department of Adaptive Systems
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101141
name1 Kroupa
name2 Tomáš
full_dept (cz) Matematická teorie rozhodování
full_dept Department of Decision Making Theory
department (cz) MTR
department MTR
institution UTIA-B
full_dept Department of Decision Making Theory
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2011/AS/karny-0367271.pdf
cas_special
project
project_id 2C06001
agency GA MŠk
country CZ
ARLID cav_un_auth*0217685
project
project_id GA102/08/0567
agency GA ČR
ARLID cav_un_auth*0239566
research CEZ:AV0Z10750506
abstract (eng) This text provides background of fully probabilistic design (FPD) of decision-making strategies and shows that it is a proper extension of the standard Bayesian decision making. FPD essentially minimises Kullback–Leibler divergence of closed-loop model on its ideal counterpart. The inspection of the background is important as the current motivation for FPD is mostly heuristic one, while the technical development of FPD confirms its far reaching possibilities. FPD unifies and simplifies subtasks and elements of decision making under uncertainty. For instance, (i) both system model and decision preferences are expressed in common probabilistic language; (ii) optimisation is simplified due to existence of explicit minimiser in stochastic dynamic programming; (iii) DM methodology for single and multiple aims is unified.
reportyear 2012
RIV BB
num_of_auth 2
mrcbC52 4 A 4a 20231122134749.9
permalink http://hdl.handle.net/11104/0202012
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arlyear 2012
mrcbTft \nSoubory v repozitáři: karny-0367271.pdf
mrcbU14 81355127223 SCOPUS
mrcbU34 000298460300007 WOS
mrcbU63 cav_un_epca*0256752 Information Sciences 0020-0255 1872-6291 Roč. 186 č. 1 2012 105 113 Elsevier