Computational Probability Applications

  • Andrew G. Glen
  • Lawrence M. Leemis

Part of the International Series in Operations Research & Management Science book series (ISOR, volume 247)

Table of contents

  1. Front Matter
    Pages i-x
  2. Andrew G. Glen, Donald R. Barr, Lawrence M. Leemis
    Pages 31-39
  3. Donald R. Barr, Andrew G. Glen, Harvey F. Graf
    Pages 41-50
  4. Andrew G. Glen, Lawrence M. Leemis, Daniel J. Luckett
    Pages 51-58
  5. Daniel J. Luckett, Samatha King, Lawrence M. Leemis
    Pages 87-106
  6. William H. Kaczynski, Lawrence M. Leemis
    Pages 107-117
  7. Lee S. McDaniel, Andrew G. Glen, Lawrence M. Leemis
    Pages 119-132
  8. Lawrence M. Leemis, Daniel J. Luckett, Austin G. Powell, Peter E. Vermeer
    Pages 133-147
  9. Erik Vargo, Raghu Pasupathy, Lawrence M. Leemis
    Pages 149-164
  10. A. Daniel Block, Lawrence M. Leemis
    Pages 191-215
  11. Back Matter
    Pages 239-256

About this book


This focuses on the developing field of building probability models with the power of symbolic algebra systems. The book combines the uses of symbolic algebra with probabilistic/stochastic application and highlights the applications in a variety of contexts. The research explored in each chapter is unified by the use of A Probability Programming Language (APPL) to achieve the modeling objectives. APPL, as a research tool, enables a probabilist or statistician the ability to explore new ideas, methods, and models. Furthermore, as an open-source language, it sets the foundation for future algorithms to augment the original code. 

Computational Probability Applications is comprised of fifteen chapters, each presenting a specific application of computational probability using the APPL modeling and computer language. The chapter topics include using inverse gamma as a survival distribution, linear approximations of probability density functions, and also moment-ratio diagrams for univariate distributions. These works highlight interesting examples, often done by undergraduate students and graduate students that can serve as templates for future work. In addition, this book should appeal to researchers and practitioners in a range of fields including probability, statistics, engineering, finance, neuroscience, and economics.


APPL Computational Probability Maple Probabilistic Applications Probability Theory Stochastic Applications

Editors and affiliations

  • Andrew G. Glen
    • 1
  • Lawrence M. Leemis
    • 2
  1. 1.Department of Mathematics and Computer ScienceThe Colorado CollegeColorado SpringsUSA
  2. 2.Department of MathematicsThe College of William and MaryWilliamsburgUSA

Bibliographic information