Abstract
In many practical situations, users select between n alternatives a 1,...,a n , and the only information that we have about the utilities v i of these alternatives are bounds \(\underline v_i\le v_i\le \overline v_i\). In such situations, it is reasonable to assume that the values v i are independent and uniformly distributed on the corresponding intervals \([\underline v_i,\overline v_i]\). Under this assumption, we would like to estimate, for each i, the probability p i that the alternative a i will be selected. In this paper, we provide efficient algorithms for computing these probabilities.
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Huynh, V.N., Kreinovich, V., Nakamori, Y., Nguyen, H.T. (2008). Towards Efficient Prediction of Decisions under Interval Uncertainty. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2007. Lecture Notes in Computer Science, vol 4967. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68111-3_145
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DOI: https://doi.org/10.1007/978-3-540-68111-3_145
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68105-2
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