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- W2513909143 abstract "We introduce a new concept of approximation applicable to decision problems and functions, inspired by Bayesian probability. From the perspective of a Bayesian reasoner with limited computational resources, the answer to a problem that cannot be solved exactly is uncertain and therefore should be described by a random variable. It thus should make sense to talk about the expected value of this random variable, an idea we formalize in the language of average-case complexity theory by introducing the concept of polynomial-time estimators. We prove some existence theorems and completeness results, and show that optimal polynomial-time estimators exhibit many parallels with classical probability theory." @default.
- W2513909143 created "2016-09-16" @default.
- W2513909143 creator A5090524750 @default.
- W2513909143 date "2016-08-14" @default.
- W2513909143 modified "2023-09-27" @default.
- W2513909143 title "Optimal Polynomial-Time Estimators: A Bayesian Notion of Approximation Algorithm." @default.
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