Solving decision models under uncertain joint distributions: Approximations, Simulation, and Accuracy of Multivariate Discrete Probability Distributions in Decision Analysis - Couverture souple

Montiel, Luis V.

 
9783659217173: Solving decision models under uncertain joint distributions: Approximations, Simulation, and Accuracy of Multivariate Discrete Probability Distributions in Decision Analysis
Couverture souple
ISBN 10 : 3659217174 ISBN 13 : 9783659217173
Editeur : LAP LAMBERT Academic Publishing, 2012

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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Présentation de l'éditeur

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.

Biographie de l'auteur

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.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2012
Langue
anglais
ISBN 10 
3659217174
ISBN 13 
9783659217173
Reliure
Broché
Nombre de pages
284
Coordonnées du fabricant
non disponible
Personne responsable
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