Computational Probability

John H. Drew, Diane L. Evans, Andrew G. Glen and Lawrence M. Leemis
Additional contact information
John H. Drew: The College of William and Mary
Diane L. Evans: Rose-Hulman Institute of Technology
Andrew G. Glen: Colorado College
Lawrence M. Leemis: The College of William and Mary

Chapter 1 in Computational Probability, 2017, pp 3-11 from Springer

Abstract: Abstract The purpose of this chapter is to lure you into reading the rest of the monograph. We present four examples of probability questions that would be unpleasant to solve by hand, but are solvable with computational probability using APPL (A Probability Programming Language). We define the field of computational probability as the development of data structures and algorithms to automate the derivation of existing and new results in probability and statistics. Section 12.3, for example, contains the derivation of the distribution of a well-known test statistic that requires 99,500 carefully crafted integrations.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-319-43323-3_1

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DOI: 10.1007/978-3-319-43323-3_1

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