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Probabilistic Forecasts and Optimal Decisions - by Roman Krzysztofowicz (Hardcover)
About this item
Highlights
- About the Author: 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.
- 576 Pages
- Technology, Operations Research
Description
About the Book
"Since its origin in the 18th century, decision theory has developed as a coherent mathematical-logical framework, and there are effective analytical tools for dealing with all major sources of the decisional difficulty. And yet making decisions can still be difficult. What makes deciding difficult?"--From the Back Cover
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.
About the Author
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.