Overview

Account for uncertainties and optimize decision-making with this thorough exposition Decision theory is a body of thought and research seeking to apply a mathematical-logical framework to assessing probability and optimizing decision-making. It has developed robust tools for addressing all major challenges to decision making. Yet the number of variables and uncertainties affecting each decision outcome, many of them beyond the decider’s control, mean that decision-making is far from a ‘solved problem’. The tools created by decision theory remain to be refined and applied to decisions in which uncertainties are prominent. Probabilistic Forecasts and Optimal Decisions introduces a theoretically-grounded methodology for optimizing decision-making under conditions of uncertainty. Beginning with an overview of the basic elements of probability theory and methods for modeling continuous variates, it proceeds to survey the mathematics of both continuous and discrete models, supporting each with key examples. The result is a crucial window into the complex but enormously rewarding world of decision theory. Readers of Probablistic Forecasts and Optimal Decisions will also find: Extended case studies supported with real-world data Mini-projects running through multiple chapters to illustrate different stages of the decision-making process End of chapter exercises designed to facilitate student learning Probabilistic Forecasts and Optimal Decisions is ideal for advanced undergraduate and graduate students in the sciences and engineering, as well as predictive analytics and decision analytics professionals.

Full Product Details

Author:   Roman Krzysztofowicz (University of Virginia)
Publisher:   John Wiley & Sons Inc
Imprint:   John Wiley & Sons Inc
Dimensions:   Width: 20.90cm , Height: 3.80cm , Length: 26.10cm
Weight:   1.501kg
ISBN:  

9781394221868

ISBN 10:   139422186
Pages:   576
Publication Date:   26 December 2024
Audience:   College/higher education ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   Out of stock  
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Table of Contents

Preface xxi About the Companion Website xxiii 1 Forecast–Decision Theory 1 Part I Elements of Probability 9 2 Basic Elements 11 3 Distribution Modeling 37 Part II Discrete Models 73 4 Judgmental Forecasting 75 5 Statistical Forecasting 109 6 Verification of Forecasts 143 7 Detection-Decision Theory 179 8 Various Discrete Models 209 Part III Continuous Models 237 9 Judgmental Forecasting 239 10 Statistical Forecasting 273 11 Verification of Forecasts 315 12 Target-Decision Theory 353 13 Inventory and Capacity Models 387 14 Investment Models 413 15 Various Continuous Models 457 A Rationality Postulates 479 B Parameter Estimation Methods 489 C Special Univariate Distributions 493 The Greek Alphabet 527 References 529 Index 535

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Author Information

Roman Krzysztofowicz, PhD, is Professor of Systems Engineering in the School of Engineering and Applied Science and Professor of Statistics in the College and Graduate School of Arts and Sciences at the University of Virginia, Charlottesville, USA. He has previously held faculty posts at the University of Arizona and MIT, and his Bayesian Forecast-Decision Theory supplies a unified framework for the design and analysis of probabilistic forecast systems coupled with optimal decision systems.

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