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Author : Martin Peterson
Publisher : Cambridge University Press
Page : 351 pages
File Size : 50,8 Mb
Release : 2017-03-30
Category : Business & Economics
ISBN : 9781107151598
A comprehensive and accessible introduction to all aspects of decision theory, now with new and updated discussions and over 140 exercises.
Author : Richard Bradley
Publisher : Cambridge University Press
Page : 351 pages
File Size : 51,6 Mb
Release : 2017-10-26
Category : Mathematics
ISBN : 9781107003217
Explores how decision-makers can manage uncertainty that varies in both kind and severity by extending and supplementing Bayesian decision theory.
Author : James O. Berger
Publisher : Springer Science & Business Media
Page : 633 pages
File Size : 40,5 Mb
Release : 2013-03-14
Category : Mathematics
ISBN : 9781475742862
In this new edition the author has added substantial material on Bayesian analysis, including lengthy new sections on such important topics as empirical and hierarchical Bayes analysis, Bayesian calculation, Bayesian communication, and group decision making. With these changes, the book can be used as a self-contained introduction to Bayesian analysis. In addition, much of the decision-theoretic portion of the text was updated, including new sections covering such modern topics as minimax multivariate (Stein) estimation.
Author : David A. Blackwell,M. A. Girshick
Publisher : Courier Corporation
Page : 388 pages
File Size : 49,5 Mb
Release : 2012-06-14
Category : Mathematics
ISBN : 9780486150895
Evaluating statistical procedures through decision and game theory, as first proposed by Neyman and Pearson and extended by Wald, is the goal of this problem-oriented text in mathematical statistics. First-year graduate students in statistics and other students with a background in statistical theory and advanced calculus will find a rigorous, thorough presentation of statistical decision theory treated as a special case of game theory. The work of Borel, von Neumann, and Morgenstern in game theory, of prime importance to decision theory, is covered in its relevant aspects: reduction of games to normal forms, the minimax theorem, and the utility theorem. With this introduction, Blackwell and Professor Girshick look at: Values and Optimal Strategies in Games; General Structure of Statistical Games; Utility and Principles of Choice; Classes of Optimal Strategies; Fixed Sample-Size Games with Finite Ω and with Finite A; Sufficient Statistics and the Invariance Principle; Sequential Games; Bayes and Minimax Sequential Procedures; Estimation; and Comparison of Experiments. A few topics not directly applicable to statistics, such as perfect information theory, are also discussed. Prerequisites for full understanding of the procedures in this book include knowledge of elementary analysis, and some familiarity with matrices, determinants, and linear dependence. For purposes of formal development, only discrete distributions are used, though continuous distributions are employed as illustrations. The number and variety of problems presented will be welcomed by all students, computer experts, and others using statistics and game theory. This comprehensive and sophisticated introduction remains one of the strongest and most useful approaches to a field which today touches areas as diverse as gambling and particle physics.
Author : James M. Joyce
Publisher : Cambridge University Press
Page : 300 pages
File Size : 40,9 Mb
Release : 1999-04-13
Category : Computers
ISBN : 0521641640
The book also contains a major new discussion of what it means to suppose that some event occurs or that some proposition is true.
Author : Herman Chernoff,Lincoln E. Moses
Publisher : Courier Corporation
Page : 386 pages
File Size : 43,7 Mb
Release : 1986-01-01
Category : Mathematics
ISBN : 0486652181
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 : James Berger
Publisher : Springer Science & Business Media
Page : 440 pages
File Size : 41,5 Mb
Release : 2013-04-17
Category : Mathematics
ISBN : 9781475717273
Decision theory is generally taught in one of two very different ways. When of opti taught by theoretical statisticians, it tends to be presented as a set of mathematical techniques mality principles, together with a collection of various statistical procedures. When useful in establishing the optimality taught by applied decision theorists, it is usually a course in Bayesian analysis, showing how this one decision principle can be applied in various practical situations. The original goal I had in writing this book was to find some middle ground. I wanted a book which discussed the more theoretical ideas and techniques of decision theory, but in a manner that was constantly oriented towards solving statistical problems. In particular, it seemed crucial to include a discussion of when and why the various decision prin ciples should be used, and indeed why decision theory is needed at all. This original goal seemed indicated by my philosophical position at the time, which can best be described as basically neutral. I felt that no one approach to decision theory (or statistics) was clearly superior to the others, and so planned a rather low key and impartial presentation of the competing ideas. In the course of writing the book, however, I turned into a rabid Bayesian. There was no single cause for this conversion; just a gradual realization that things seemed to ultimately make sense only when looked at from the Bayesian viewpoint.
Author : John Pratt,Howard Raiffa,Robert Schlaifer
Publisher : MIT Press
Page : 0 pages
File Size : 42,6 Mb
Release : 2008-01-25
Category : Business & Economics
ISBN : 9780262662062
The Bayesian revolution in statistics—where statistics is integrated with decision making in areas such as management, public policy, engineering, and clinical medicine—is here to stay. Introduction to Statistical Decision Theory states the case and in a self-contained, comprehensive way shows how the approach is operational and relevant for real-world decision making under uncertainty. Starting with an extensive account of the foundations of decision theory, the authors develop the intertwining concepts of subjective probability and utility. They then systematically and comprehensively examine the Bernoulli, Poisson, and Normal (univariate and multivariate) data generating processes. For each process they consider how prior judgments about the uncertain parameters of the process are modified given the results of statistical sampling, and they investigate typical decision problems in which the main sources of uncertainty are the population parameters. They also discuss the value of sampling information and optimal sample sizes given sampling costs and the economics of the terminal decision problems. Unlike most introductory texts in statistics, Introduction to Statistical Decision Theory integrates statistical inference with decision making and discusses real-world actions involving economic payoffs and risks. After developing the rationale and demonstrating the power and relevance of the subjective, decision approach, the text also examines and critiques the limitations of the objective, classical approach.
Author : F. Liese,Klaus-J. Miescke
Publisher : Springer Science & Business Media
Page : 696 pages
File Size : 49,9 Mb
Release : 2008-12-30
Category : Mathematics
ISBN : 9780387731940
For advanced graduate students, this book is a one-stop shop that presents the main ideas of decision theory in an organized, balanced, and mathematically rigorous manner, while observing statistical relevance. All of the major topics are introduced at an elementary level, then developed incrementally to higher levels. The book is self-contained as it provides full proofs, worked-out examples, and problems. The authors present a rigorous account of the concepts and a broad treatment of the major results of classical finite sample size decision theory and modern asymptotic decision theory. With its broad coverage of decision theory, this book fills the gap between standard graduate texts in mathematical statistics and advanced monographs on modern asymptotic theory.
Author : Itzhak Gilboa
Publisher : Cambridge University Press
Page : 216 pages
File Size : 53,7 Mb
Release : 2009-03-16
Category : Business & Economics
ISBN : 9780521517324
This book describes the classical axiomatic theories of decision under uncertainty, as well as critiques thereof and alternative theories. It focuses on the meaning of probability, discussing some definitions and surveying their scope of applicability. The behavioral definition of subjective probability serves as a way to present the classical theories, culminating in Savage's theorem. The limitations of this result as a definition of probability lead to two directions - first, similar behavioral definitions of more general theories, such as non-additive probabilities and multiple priors, and second, cognitive derivations based on case-based techniques.
Author : Giovanni Parmigiani,Lurdes Inoue
Publisher : John Wiley & Sons
Page : 416 pages
File Size : 50,9 Mb
Release : 2009-05-26
Category : Business & Economics
ISBN : UOM:39015080846135
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 : José Luis Bermúdez
Publisher : OUP Oxford
Page : 199 pages
File Size : 49,6 Mb
Release : 2009-02-19
Category : Philosophy
ISBN : 9780191567728
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.
Author : Robin M. Hogarth
Publisher : University of Chicago Press
Page : 376 pages
File Size : 53,9 Mb
Release : 1990-04-18
Category : Business & Economics
ISBN : 0226348555
How do people make decisions? How can we help people make better decisions? How can we best study the processes of decision making? The growing field of behavioral decision research, which seeks to link observed decision behavior to underlying psychological mechanisms, may provide the answers to these questions. The volume is based on a recent conference held to honor the work and memory of the late Hillel J. Einhorn, a pioneering scholar in behavioral decision research. Composed of contributions by leading researchers, Insights in Decision Making provides a state-of-the-art image of work in this field. The range of topics covered includes conceptual and technical issues the bridge the gap between theory and the practical concern of improving decision making, difficulties in statistical thinking, experimental studies of processes of judgment and choice, and the emergence of new paradigms for studying decision behavior. Providing many avenues for future research, Insights in Decision Making will be essential reading for students of the psychology of decision making and will prove valuable to readers in psychology, economics, statistics, and management.
Author : Kazuhisa Takemura
Publisher : Springer Nature
Page : 389 pages
File Size : 45,8 Mb
Release : 2021-09-29
Category : Business & Economics
ISBN : 9789811654534
This book is the second edition of Behavioral Decision Theory, published in 2014. The main approach and structure of this book have been retained in the new edition. However, this second edition provides a fresh overview of the idea of behavioral decision theory and related research findings such as theoretical and empirical discoveries of preference formation, time discounting, social interaction, and social decision making. The book covers a wide range from classical to relatively recent major studies concerning behavioral decision theory, which, in brief, is a general term for descriptive theories to explain the psychological knowledge related to people’s decision-making behavior. It is called a theory but is actually a combination of various psychological theories, for which no axiomatic systems—such as those associated with the utility theory widely used in economics—have been established. The utility theory is often limited to qualitative knowledge; however, as the studies of Nobel laureates H. A. Simon, D. Kahneman, and R. Thaler have suggested, the psychological methodology and knowledge of behavioral decision theory have been applied widely in such fields as economics, business administration, and engineering and are expected to become even more useful in the future. Research into people’s decision making represents an important part in those fields, various aspects of which overlap with the scope of behavioral decision theory. This theory is closely related to behavioral economics and behavioral finance, which have come into greater use in recent years. This book will appeal especially to graduate students, advanced undergraduate students, and researchers who are interested in decision-making phenomena.
Author : Silvia Bacci,Bruno Chiandotto
Publisher : CRC Press
Page : 305 pages
File Size : 47,8 Mb
Release : 2019-07-11
Category : Mathematics
ISBN : 9781351621397
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