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Solving decision models under uncertain joint distributions: Approximations, Simulation, and Accuracy of Multivariate Discrete Probability Distributions in Decision Analysis - Softcover
Synopsis
Almost all relevant decisions have attached values or outcomes that are uncertain. However, when a complete description of the uncertainties is unknown, traditional models struggle to provide an optimal course of action. In this book, I present a modeling procedure to analyze stochastic decisions with underspecified joint probability distributions. The book is directed to researchers and practitioners with the interest of modeling problems where the structure of the uncertainties is partially known. This work represents a four-fold project. First, a new framework for joint probability distribution approximations is provided. Second, a new joint distribution simulation procedure (JDSIM) is developed. JDSIM sample joint probability distributions from the set of all possible distributions that match the available information. Third, a framework for testing the accuracy of different joint probability distribution approximations is developed. Finally, a new approach to decision making under uncertainty is proposed. The techniques in this book will provide the reader with the tools to model and analyze decisions with a new and deeper understanding of the relevant uncertainties.
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About the Author
Born in Mexico. He has a BS in Engineering from ITESM-CCM, a MS in Management Science & Engineering at Stanford University, and a MS in Financial Engineering at Columbia University. He holds a Ph. D. in Operations Research from The University of Texas at Austin. His main interests are stochastic optimization and decision analysis.
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Solving decision models under uncertain joint distributions
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LAP LAMBERT Academic Publishing Aug 2012, 2012
ISBN 10: 3659217174
ISBN 13: 9783659217173
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Almost all relevant decisions have attached values or outcomes that are uncertain. However, when a complete description of the uncertainties is unknown, traditional models struggle to provide an optimal course of action. In this book, I present a modeling procedure to analyze stochastic decisions with underspecified joint probability distributions. The book is directed to researchers and practitioners with the interest of modeling problems where the structure of the uncertainties is partially known. This work represents a four-fold project. First, a new framework for joint probability distribution approximations is provided. Second, a new joint distribution simulation procedure (JDSIM) is developed. JDSIM sample joint probability distributions from the set of all possible distributions that match the available information. Third, a framework for testing the accuracy of different joint probability distribution approximations is developed. Finally, a new approach to decision making under uncertainty is proposed. The techniques in this book will provide the reader with the tools to model and analyze decisions with a new and deeper understanding of the relevant uncertainties. 284 pp. Englisch. Seller Inventory # 9783659217173
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Solving decision models under uncertain joint distributions
Published by
LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3659217174
ISBN 13: 9783659217173
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Montiel Luis V.Born in Mexico. He has a BS in Engineering from ITESM-CCM, a MS in Management Science & Engineering at Stanford University, and a MS in Financial Engineering at Columbia University. He holds a Ph. D. in Operations Rese. Seller Inventory # 5140489
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Solving decision models under uncertain joint distributions
Published by
LAP LAMBERT Academic Publishing Aug 2012, 2012
ISBN 10: 3659217174
ISBN 13: 9783659217173
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Neuware -Almost all relevant decisions have attached values or outcomes that are uncertain. However, when a complete description of the uncertainties is unknown, traditional models struggle to provide an optimal course of action. In this book, I present a modeling procedure to analyze stochastic decisions with underspecified joint probability distributions. The book is directed to researchers and practitioners with the interest of modeling problems where the structure of the uncertainties is partially known. This work represents a four-fold project. First, a new framework for joint probability distribution approximations is provided. Second, a new joint distribution simulation procedure (JDSIM) is developed. JDSIM sample joint probability distributions from the set of all possible distributions that match the available information. Third, a framework for testing the accuracy of different joint probability distribution approximations is developed. Finally, a new approach to decision making under uncertainty is proposed. The techniques in this book will provide the reader with the tools to model and analyze decisions with a new and deeper understanding of the relevant uncertainties.Books on Demand GmbH, Überseering 33, 22297 Hamburg 284 pp. Englisch. Seller Inventory # 9783659217173
Quantity: 2 available
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Solving decision models under uncertain joint distributions : Approximations, Simulation, and Accuracy of Multivariate Discrete Probability Distributions in Decision Analysis
Published by
LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3659217174
ISBN 13: 9783659217173
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Almost all relevant decisions have attached values or outcomes that are uncertain. However, when a complete description of the uncertainties is unknown, traditional models struggle to provide an optimal course of action. In this book, I present a modeling procedure to analyze stochastic decisions with underspecified joint probability distributions. The book is directed to researchers and practitioners with the interest of modeling problems where the structure of the uncertainties is partially known. This work represents a four-fold project. First, a new framework for joint probability distribution approximations is provided. Second, a new joint distribution simulation procedure (JDSIM) is developed. JDSIM sample joint probability distributions from the set of all possible distributions that match the available information. Third, a framework for testing the accuracy of different joint probability distribution approximations is developed. Finally, a new approach to decision making under uncertainty is proposed. The techniques in this book will provide the reader with the tools to model and analyze decisions with a new and deeper understanding of the relevant uncertainties. Seller Inventory # 9783659217173
Quantity: 1 available
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Solving decision models under uncertain joint distributions: Approximations, Simulation, and Accuracy of Multivariate Discrete Probability Distributions in Decision Analysis
Published by
LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3659217174
ISBN 13: 9783659217173
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