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Decision-making under uncertainty processed by lattice-valued possibilistic measures
Kybernetika, Tome 42 (2006) no. 6, pp. 629-646 Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

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The notion and theory of statistical decision functions are re-considered and modified to the case when the uncertainties in question are quantified and processed using lattice-valued possibilistic measures, so emphasizing rather the qualitative than the quantitative properties of the resulting possibilistic decision functions. Possibilistic variants of both the minimax (the worst-case) and the Bayesian optimization principles are introduced and analyzed.
The notion and theory of statistical decision functions are re-considered and modified to the case when the uncertainties in question are quantified and processed using lattice-valued possibilistic measures, so emphasizing rather the qualitative than the quantitative properties of the resulting possibilistic decision functions. Possibilistic variants of both the minimax (the worst-case) and the Bayesian optimization principles are introduced and analyzed.
Classification : 28E10, 28E99, 91B06
Keywords: decision making under uncertainty; complete lattice; lattice- valued possibilistic measures; possibilistic decision function; minimax and Bayesian optimization 
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Kramosil, Ivan. Decision-making under uncertainty processed by lattice-valued possibilistic measures. Kybernetika, Tome 42 (2006) no. 6, pp. 629-646. http://geodesic.mathdoc.fr/item/KYB_2006_42_6_a0/

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