Computational Probability Applications

Lawrence M. Leemis

ISBN 10: 331982788X / ISBN 13: 9783319827889

Verlag: Springer International Publishing, Springer Nature Switzerland Jul 2018, 2018

Beschreibung

Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 268 pp. Englisch.

Bestandsnummer des Verkäufers 9783319827889

Bibliografische Details

• Titel: Computational Probability Applications
• Verlag: Springer International Publishing, Springer Nature Switzerland Jul 2018
• Erscheinungsjahr: 2018
• Sprache: Englisch
• ISBN-10: 331982788X
• ISBN-13: 9783319827889
• Einband: Taschenbuch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

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.

Über die Autorin bzw. den Autor

Dr. Andrew Glen is a Professor Emeritus of Operations Research from the United States Military Academy, in West Point, NY. He is currently a visiting professor at The Colorado College in Colorado Springs, Colorado. He is a retired colonel from the US Army, and spend 16 years on faculty at West Point. He has published three books and dozens of scholarly articles, mostly on the subject of computational probability. His research and teaching interests are in computational probability and statistical modeling. 
Lawrence Leemis is a professor in the Department of Mathematics at The College of William & Mary in Williamsburg, Virginia, U.S.A. He received his BS and MS degrees in mathematics and his PhD in operations research from Purdue University. He has also taught courses at Purdue University, The University of Oklahoma, and Baylor University. He has served as Associate Editor for the IEEE Transactions on Reliability, Book Review Editor for the Journal of Quality Technology, and an Associate Editor for Naval Research Logistics. He has published six books and over 100 research articles, proceedings papers, and book chapters. His research and teaching interests are in reliability, simulation, and computational probability.