Abstract
A hierarchical uncertainty model for combining the different judgements is studied in the paper. The model is general enough for many applications. The presented approach for dealing with such model allows us to combine the available heterogeneous information in the following ways: computing new probability bounds for some predefined interval of previsions, computing an “average” interval of first-order previsions, and updating the second-order probabilities after observing new event.
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Utkin, L.V. (2002). A Hierarchical Uncertainty Model under Essentially Incomplete Information. In: Grzegorzewski, P., Hryniewicz, O., Gil, M.Á. (eds) Soft Methods in Probability, Statistics and Data Analysis. Advances in Intelligent and Soft Computing, vol 16. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1773-7_14
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DOI: https://doi.org/10.1007/978-3-7908-1773-7_14
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1526-9
Online ISBN: 978-3-7908-1773-7
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