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- W3043996861 abstract "In distributional or average-case analysis, the goal is to design an algorithm with good-on-average performance with respect to a specific probability distribution. Distributional analysis can be useful for the study of general-purpose algorithms on non-pathological inputs, and for the design of specialized algorithms in applications in which there is detailed understanding of the relevant input distribution. For some problems, however, pure distributional analysis encourages overfitting an algorithmic solution to a particular distributional assumption and a more robust analysis framework is called for. This chapter presents numerous examples of the pros and cons of distributional analysis, highlighting some of its greatest hits while also setting the stage for the hybrids of worst- and average-case analysis studied in later chapters." @default.
- W3043996861 created "2020-07-29" @default.
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- W3043996861 date "2014-01-01" @default.
- W3043996861 modified "2023-09-27" @default.
- W3043996861 title "Distributional Analysis" @default.
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- W3043996861 doi "https://doi.org/10.1007/978-1-4614-5690-2_100187" @default.
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