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- W1981844345 abstract "In many real-life situations, we only have partial information about the actual probability distribution. For example, under Dempster–Shafer uncertainty, we only know the masses m1, … ,mn assigned to different sets S1, … ,Sn, but we do not know the distribution within each set Si. Because of this uncertainty, there are many possible probability distributions consistent with our knowledge; different distributions have, in general, different values of standard statistical characteristics such as mean and variance. It is therefore desirable, given a Dempster–Shafer knowledge base, to compute the ranges [E̲,E¯] and [V̲,V¯] of possible values of mean E and of variance V. In their recent paper, Langewisch and Choobineh show how to compute these ranges in polynomial time. In particular, they reduce the problem of computing V¯ to the problem of minimizing a convex quadratic function, a problem which can be solved in time O(n2 · log(n)). We show that the corresponding quadratic optimization problem can be actually solved faster, in time O(n · log(n)); thus, we can compute the bounds V and V¯ in time O(n · log(n))." @default.
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- W1981844345 date "2006-08-01" @default.
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- W1981844345 title "Computing mean and variance under Dempster–Shafer uncertainty: Towards faster algorithms" @default.
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- W1981844345 doi "https://doi.org/10.1016/j.ijar.2005.12.001" @default.
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