An Introduction to Decision Theory PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download An Introduction to Decision Theory PDF full book. Access full book title An Introduction to Decision Theory by Martin Peterson. Download full books in PDF and EPUB format.
Author: Martin Peterson
Publisher: Cambridge University Press
ISBN: 1107151597
Category : Business & Economics
Languages : en
Pages : 351
Get Book Here
Book Description
A comprehensive and accessible introduction to all aspects of decision theory, now with new and updated discussions and over 140 exercises.
Author: Martin Peterson
Publisher: Cambridge University Press
ISBN: 1107151597
Category : Business & Economics
Languages : en
Pages : 351
Get Book Here
Book Description
A comprehensive and accessible introduction to all aspects of decision theory, now with new and updated discussions and over 140 exercises.
Author: Giovanni Parmigiani
Publisher: John Wiley & Sons
ISBN:
Category : Business & Economics
Languages : en
Pages : 416
Get Book Here
Book Description
Decision theory provides a formal framework for making logical choices in the face of uncertainty. Given a set of alternatives, a set of consequences, and a correspondence between those sets, decision theory offers conceptually simple procedures for choice. This book presents an overview of the fundamental concepts and outcomes of rational decision making under uncertainty, highlighting the implications for statistical practice. The authors have developed a series of self contained chapters focusing on bridging the gaps between the different fields that have contributed to rational decision making and presenting ideas in a unified framework and notation while respecting and highlighting the different and sometimes conflicting perspectives. This book: * Provides a rich collection of techniques and procedures. * Discusses the foundational aspects and modern day practice. * Links foundations to practical applications in biostatistics, computer science, engineering and economics. * Presents different perspectives and controversies to encourage readers to form their own opinion of decision making and statistics. Decision Theory is fundamental to all scientific disciplines, including biostatistics, computer science, economics and engineering. Anyone interested in the whys and wherefores of statistical science will find much to enjoy in this book.
Author: John Winsor Pratt
Publisher:
ISBN:
Category : Statistical Decision
Languages : en
Pages : 875
Get Book Here
Book Description
Author: Anthony Kelly
Publisher: Cambridge University Press
ISBN: 9781139438131
Category : Business & Economics
Languages : en
Pages : 228
Get Book Here
Book Description
Game theory is a key element in most decision-making processes involving two or more people or organisations. This book explains how game theory can predict the outcome of complex decision-making processes, and how it can help you to improve your own negotiation and decision-making skills. It is grounded in well-established theory, yet the wide-ranging international examples used to illustrate its application offer a fresh approach to an essential weapon in the armoury of the informed manager. The book is accessibly written, explaining in simple terms the underlying mathematics behind games of skill, before moving on to more sophisticated topics such as zero-sum games, mixed-motive games, and multi-person games, coalitions and power. Clear examples and helpful diagrams are used throughout, and the mathematics is kept to a minimum. It is written for managers, students and decision makers in any field.
Author: Mykel J. Kochenderfer
Publisher: MIT Press
ISBN: 0262331713
Category : Computers
Languages : en
Pages : 350
Get Book Here
Book Description
An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.
Author: Herman Chernoff
Publisher: Courier Corporation
ISBN: 9780486652184
Category : Mathematics
Languages : en
Pages : 386
Get Book Here
Book Description
This well-respected introduction to statistics and statistical theory covers data processing, probability and random variables, utility and descriptive statistics, computation of Bayes strategies, models, testing hypotheses, and much more. 1959 edition.
Author: Richard Bradley
Publisher: Cambridge University Press
ISBN: 1107003210
Category : Business & Economics
Languages : en
Pages : 351
Get Book Here
Book Description
Explores how decision-makers can manage uncertainty that varies in both kind and severity by extending and supplementing Bayesian decision theory.
Author: Silvia Bacci
Publisher: CRC Press
ISBN: 1351621386
Category : Mathematics
Languages : en
Pages : 292
Get Book Here
Book Description
Introduction to Statistical Decision Theory: Utility Theory and Causal Analysis provides the theoretical background to approach decision theory from a statistical perspective. It covers both traditional approaches, in terms of value theory and expected utility theory, and recent developments, in terms of causal inference. The book is specifically designed to appeal to students and researchers that intend to acquire a knowledge of statistical science based on decision theory. Features Covers approaches for making decisions under certainty, risk, and uncertainty Illustrates expected utility theory and its extensions Describes approaches to elicit the utility function Reviews classical and Bayesian approaches to statistical inference based on decision theory Discusses the role of causal analysis in statistical decision theory
Author: Simon French
Publisher: Prentice Hall
ISBN: 9780132010139
Category : Science
Languages : en
Pages : 448
Get Book Here
Book Description
Author: José Luis Bermúdez
Publisher: OUP Oxford
ISBN: 0191609455
Category : Philosophy
Languages : en
Pages : 208
Get Book Here
Book Description
The concept of rationality is a common thread through the human and social sciences — from political science to philosophy, from economics to sociology, and from management science to decision analysis. But what counts as rational action and rational behavior? José Luis Bermúdez explores decision theory as a theory of rationality. Decision theory is the mathematical theory of choice and for many social scientists it makes the concept of rationality mathematically tractable and scientifically legitimate. Yet rationality is a concept with several dimensions and the theory of rationality has different roles to play. It plays an action-guiding role (prescribing what counts as a rational solution of a given decision problem). It plays a normative role (giving us the tools to pass judgment not just on how a decision problem was solved, but also on how it was set up in the first place). And it plays a predictive/explanatory role (telling us how rational agents will behave, or why they did what they did). This controversial but accessible book shows that decision theory cannot play all of these roles simultaneously. And yet, it argues, no theory of rationality can play one role without playing the other two. The conclusion is that there is no hope of taking decision theory as a theory of rationality.
