Computational Probability

  • John H. Drew
  • Diane L. Evans
  • Andrew G. Glen
  • Lawrence M. Leemis
Chapter
First Online:
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 246)

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.

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References

  1. 52.
    Hogg RV, McKean JW, Craig AT (2005) Introduction to the mathematical statistics, 6th edn. Prentice-Hall, Upper Saddle River, New JerseyGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • John H. Drew
    • 1
  • Diane L. Evans
    • 2
  • Andrew G. Glen
    • 3
  • Lawrence M. Leemis
    • 1
  1. 1.Department of MathematicsThe College of William and MaryWilliamsburgUSA
  2. 2.Department of MathematicsRose-Hulman Institute of TechnologyTerre HauteUSA
  3. 3.Department of Mathematics and Computer ScienceColorado CollegeColorado SpringsUSA

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