Computational Probability Computational Probability
International Series in Operations Research & Management Science

Computational Probability

Algorithms and Applications in the Mathematical Sciences

John H. Drew والمزيد
    • ‏109٫99 US$
    • ‏109٫99 US$

وصف الناشر

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.

النوع
تمويل شركات وأفراد
تاريخ النشر
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١٥ ديسمبر
اللغة
EN
الإنجليزية
عدد الصفحات
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الناشر
Springer International Publishing
البائع
Springer Nature B.V.
الحجم
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‫م.ب.‬
Elements of Simulation Elements of Simulation
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Random-Like Multiple Objective Decision Making Random-Like Multiple Objective Decision Making
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Data Science for Business and Decision Making Data Science for Business and Decision Making
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Computer Intensive Methods in Statistics Computer Intensive Methods in Statistics
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Uncertainty Quantification and Stochastic Modelling with EXCEL Uncertainty Quantification and Stochastic Modelling with EXCEL
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Time Series Analysis and Forecasting Time Series Analysis and Forecasting
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Public Systems Modeling Public Systems Modeling
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Business Analytics Business Analytics
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Hidden Markov Models in Finance Hidden Markov Models in Finance
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Linear Programming Linear Programming
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Measuring Time Measuring Time
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Game Theory and Business Applications Game Theory and Business Applications
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