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Computational probability = algorithms and applications in the mathematical sciences /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Computational probability/ by John H. Drew ... [et al.].
Reminder of title:	algorithms and applications in the mathematical sciences /
other author:	Drew, John H.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	xi, 336 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Business. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-43323-3
ISBN:	9783319433233

Computational probability = algorithms and applications in the mathematical sciences /
Computational probabilityalgorithms and applications in the mathematical sciences /[electronic resource] :by John H. Drew ... [et al.]. - 2nd ed. - Cham :Springer International Publishing :2017. - xi, 336 p. :ill., digital ;24 cm. - International series in operations research & management science,v.2460884-8289 ;. - International series in operations research & management science ;106..

Computational Probability -- Maple for APPL -- Data Structures and Simple Algorithms -- Transformations of Random Variables -- Bivariate Transformations of Random Variables -- Products of Random Variables -- Data Structures and Simple Algorithms -- Sums of Independent Discrete Random Variables -- Order Statistics for Random Sampling from Discrete Populations -- Reliability and Survival Analysis -- Symbolic ARMA Model Analysis -- Stochastic Simulation -- Transient Queueing Analysis -- Bayesian Applications -- Other Applications.

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.

ISBN: 9783319433233

Standard No.: 10.1007/978-3-319-43323-3doiSubjects--Topical Terms:

558617
Business.

LC Class. No.: QA273.19.E4 / D74 2017

Dewey Class. No.: 658.40301

Computational probability = algorithms and applications in the mathematical sciences /
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520 $a 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.
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