Distributional Analysis
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.
Cite
@article{arxiv.2007.13240,
title = {Distributional Analysis},
author = {Tim Roughgarden},
journal= {arXiv preprint arXiv:2007.13240},
year = {2020}
}
Comments
Chapter 8 of the book Beyond the Worst-Case Analysis of Algorithms, edited by Tim Roughgarden and published by Cambridge University Press (2020)