Decision theory : an introduction to dynamic programming and sequential decisions
著者
書誌事項
Decision theory : an introduction to dynamic programming and sequential decisions
（Wiley-Interscience series in systems and optimization）
Wiley, c2000
- : hc
- : pbk
大学図書館所蔵 件 / 全36件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
Includes bibliographical references (p. [185]-186) and index
内容説明・目次
- 巻冊次
-
: hc ISBN 9780471976486
内容説明
Decision Theory An Introduction to Dynamic Programming and Sequential Decisions John Bather University of Sussex, UK Mathematical induction, and its use in solving optimization problems, is a topic of great interest with many applications. It enables us to study multistage decision problems by proceeding backwards in time, using a method called dynamic programming. All the techniques needed to solve the various problems are explained, and the author's fluent style will leave the reader with an avid interest in the subject.
* Tailored to the needs of students of optimization and decision theory
* Written in a lucid style with numerous examples and applications
* Coverage of deterministic models: maximizing utilities, directed networks, shortest paths, critical path analysis, scheduling and convexity
* Coverage of stochastic models: stochastic dynamic programming, optimal stopping problems and other special topics
* Coverage of advanced topics: Markov decision processes, minimizing expected costs, policy improvements and problems with unknown statistical parameters
* Contains exercises at the end of each chapter, with hints in an appendix
Aimed primarily at students of mathematics and statistics, the lucid text will also appeal to engineering and science students and those working in the areas of optimization and operations research.
目次
- Introduction
- PART I: Deterministic Models
- Multi-Stage Decision Problems
- Networks
- Further Applications
- Convexity
- PART II: Stochastic Models
- General Principles
- Optimal Stopping
- Special Problems
- PART III: Markov Decision Processes
- General Theory
- Minimising Average Costs
- Statistical Decision
- 巻冊次
-
: pbk ISBN 9780471976493
内容説明
Decision Theory An Introduction to Dynamic Programming and Sequential Decisions John Bather University of Sussex, UK Mathematical induction, and its use in solving optimization problems, is a topic of great interest with many applications. It enables us to study multistage decision problems by proceeding backwards in time, using a method called dynamic programming. All the techniques needed to solve the various problems are explained, and the author's fluent style will leave the reader with an avid interest in the subject.
* Tailored to the needs of students of optimization and decision theory
* Written in a lucid style with numerous examples and applications
* Coverage of deterministic models: maximizing utilities, directed networks, shortest paths, critical path analysis, scheduling and convexity
* Coverage of stochastic models: stochastic dynamic programming, optimal stopping problems and other special topics
* Coverage of advanced topics: Markov decision processes, minimizing expected costs, policy improvements and problems with unknown statistical parameters
* Contains exercises at the end of each chapter, with hints in an appendix
Aimed primarily at students of mathematics and statistics, the lucid text will also appeal to engineering and science students and those working in the areas of optimization and operations research.
目次
Preface xi
1 Introduction 1
1.1 Mathematical Induction 1
1.2 Historical Background 2
1.3 Dynamic Programming 5
1.4 The Executioner's Tale 8
1.5 Summary 8
Exercises 10
I Deterministic Models 11
2 Multi-Stage Decision Problems 13
2.1 Maximizing Utilities 13
2.2 A General Model 17
2.3 Applications 19
Exercises 25
3 Networks 27
3.1 Shortest Paths 27
3.2 Directed Networks 29
3.3 Critical Path Analysis 30
Exercises 37
4 Further Applications 39
4.1 Discrete Actions 39
4.2 The Knapsack Problem 39
4.3 A Simple Replacement Model 42
4.4 Scheduling Problems 44
4.5 Johnson's Algorithm 45
Exercises 49
5 Convexity 51
5.1 Convex and Concave Functions 51
5.2 Allocation Problems 56
5.3 Concave Utility Functions 60
Exercises 64
II Stochastic Models 67
6 Markov Systems 69
6.1 Introduction 69
6.2 Stochastic Dynamic Programming 70
6.3 Applications 72
Exercises 78
7 Optimal Stopping 79
7.1 Introduction 79
7.2 Stopping Times and Stopping Sets 82
7.3 Applications 90
Exercises 94
8 Special Problems 97
8.1 Introduction 97
8.2 Selling an Asset 97
8.3 The Marriage Problem 104
8.4 Prophet Inequalities 109
Exercises 116
III Markov Decision Processes 119
9 General Theory 121
9.1 Introduction 121
9.2 Minimizing Discounted Expectations 122
9.3 Policy Improvements 130
9.4 A Machine Replacement Model 137
10 Minimizing Average Costs 145
10.1 Introduction 145
10.2 Long-Term Average Costs 148
10.3 Extension to Infinitely Many States 153
10.4 Optimal Inventory Policies 158
11 Statistical Decisions 165
11.1 Introduction 165
11.2 Testing Statistical Hypotheses 166
11.3 The Sequential Probability Ratio Test 170
Notes On the Exercises 177
Chapter 1 177
Chapter 2 177
Chapter 3 178
Chapter 4 179
Chapter 5 179
Chapter 6 180
Chapter 7 181
Chapter 8 183
References 185
Index 187
「Nielsen BookData」 より