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- W4285277294 abstract "While Chapter 1 was focused on the choice of a model, here we examine goal-oriented optimization, i.e., the search for probability measures that optimize an assigned performance parameter under given constraints. The simple, yet fairly general, system performance index we consider here, and denote by η, is the expected value of a known function h(⋅)⩾0, computed with respect to the probability measure P of a random variable X,η≜EP[h(X)]. When P is known, η can be computed exactly. Here we are interested in a situation where epistemic uncertainty makes P only partially known, in which case we may settle for the computation of an approximation, or, better, of upper and lower bounds on η, consistent with the partial knowledge we have about P. Whenever available, we are specially interested in sharp bounds, i.e., bounds that cannot be further tightened unless additional knowledge is gathered. A special case, which will be dealt with in Chapter 3, is the derivation of sharp upper and lower bounds on η when X is only known through some of its moments." @default.
- W4285277294 created "2022-07-14" @default.
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- W4285277294 date "2022-01-01" @default.
- W4285277294 modified "2023-09-26" @default.
- W4285277294 title "Performance bounds from epistemic uncertainty" @default.
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- W4285277294 doi "https://doi.org/10.1016/b978-0-32-399275-6.00010-1" @default.
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