Abstract
The paper outlines a new approach developed in the framework of imprecise prevision theory. This approach uses expert-provided bounds as constraints on the values of probability density functions. Such approach allows us to overcome the difficulties caused by using traditional imprecise reasoning technique: by eliminating non-physical degenerate distributions, we reduce the widths of the resulting interval estimates and thus, make these estimates more practically useful.
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Krymsky, V.G. (2006). Computing Interval Bounds for Statistical Characteristics Under Expert-Provided Bounds on Probability Density Functions. In: Dongarra, J., Madsen, K., Waśniewski, J. (eds) Applied Parallel Computing. State of the Art in Scientific Computing. PARA 2004. Lecture Notes in Computer Science, vol 3732. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558958_17
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DOI: https://doi.org/10.1007/11558958_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29067-4
Online ISBN: 978-3-540-33498-9
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