Abstract
A model of a complicated function under uncertainty is constructed axiomatically, formalizing suppositions on rationality of information on a considered function.
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References
T. Fine, Theories of Probability (Academic Press, New York, 1973).
A. Kolmogoroff, Grundbegriffe der Wahrscheinlichkeitsrechnung (1933).
B. Koopman, The bases of probability, Bull. Amer. Math. Soc. 46(1940)763.
R. Luce, On numerical representation of qualitative conditional probability, Ann. Math. Stat. 39(1968)481.
J. Mockus, On Bayesian methods of seeking the extremum and their applications, in: Information Processing 77, ed. B. Gilchrist (North-Holland, Amsterdam, 1977)195.
A. Žilinskas, On statistical models for multimodal optimization, Math. Oper. Stat. 9(1978)255.
A. Žilinskas. The use of statistical models for construction of multimodal optimization algorithms, in: Third Czechoslovak-Soviet-Hungarian Seminar on Information Theory (Akademia, Prague, 1980) p. 219.
A. Žilinskas, Axiomatic approach to statistical models and their use in multimodal optimization theory, Math. Progr. 22(1982)104.
A. Žilinskas and A. Katkauskaite, Construction of statistical models of functions under uncertainty. Theory of Optimal Decisions, vol. 3 (Inst. Math. Cybern., Vilnius. 1977) p. 19 (in Russian).
A. Žilinskas and A. Katkauskaite, On the existence of a stochastic function compatible with the relation of conditional likelihood. Cybernetics, No. 4 (1982) p. 80 (in Russian).
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Žilinskas, A. On justification of use of stochastic functions for multimodal optimization models. Ann Oper Res 1, 129–134 (1984). https://doi.org/10.1007/BF01876143
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DOI: https://doi.org/10.1007/BF01876143