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This new edition includes the latest advances and developments in
computational probability involving A Probability Programming
Language (APPL). The book examines and presents, in a systematic
manner, computational probability methods that encompass data
structures and algorithms. The developed techniques address
problems that require exact probability calculations, many of which
have been considered intractable in the past. The book addresses
the plight of the probabilist by providing algorithms to perform
calculations associated with random variables. Computational
Probability: Algorithms and Applications in the Mathematical
Sciences, 2nd Edition begins with an introductory chapter that
contains short examples involving the elementary use of APPL.
Chapter 2 reviews the Maple data structures and functions necessary
to implement APPL. This is followed by a discussion of the
development of the data structures and algorithms (Chapters 3-6 for
continuous random variables and Chapters 7-9 for discrete random
variables) used in APPL. The book concludes with Chapters 10-15
introducing a sampling of various applications in the mathematical
sciences. This book should appeal to researchers in the
mathematical sciences with an interest in applied probability and
instructors using the book for a special topics course in
computational probability taught in a mathematics, statistics,
operations research, management science, or industrial engineering
department.
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.
This new edition includes the latest advances and developments in
computational probability involving A Probability Programming
Language (APPL). The book examines and presents, in a systematic
manner, computational probability methods that encompass data
structures and algorithms. The developed techniques address
problems that require exact probability calculations, many of which
have been considered intractable in the past. The book addresses
the plight of the probabilist by providing algorithms to perform
calculations associated with random variables. Computational
Probability: Algorithms and Applications in the Mathematical
Sciences, 2nd Edition begins with an introductory chapter that
contains short examples involving the elementary use of APPL.
Chapter 2 reviews the Maple data structures and functions necessary
to implement APPL. This is followed by a discussion of the
development of the data structures and algorithms (Chapters 3-6 for
continuous random variables and Chapters 7-9 for discrete random
variables) used in APPL. The book concludes with Chapters 10-15
introducing a sampling of various applications in the mathematical
sciences. This book should appeal to researchers in the
mathematical sciences with an interest in applied probability and
instructors using the book for a special topics course in
computational probability taught in a mathematics, statistics,
operations research, management science, or industrial engineering
department.
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.
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