Optimal Learning ( Wiley Series in Probability and Statistics )

Publication series :Wiley Series in Probability and Statistics

Author: Warren B. Powell  

Publisher: John Wiley & Sons Inc‎

Publication year: 2012

E-ISBN: 9781118309827

P-ISBN(Hardback):  9780470596692

Subject: TP181 automatic reasoning, machine learning

Language: ENG

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Description

Learn the science of collecting information to make effective decisions

Everyday decisions are made without the benefit of accurate information. Optimal Learning develops the needed principles for gathering information to make decisions, especially when collecting information is time-consuming and expensive. Designed for readers with an elementary background in probability and statistics, the book presents effective and practical policies illustrated in a wide range of applications, from energy, homeland security, and transportation to engineering, health, and business.

This book covers the fundamental dimensions of a learning problem and presents a simple method for testing and comparing policies for learning. Special attention is given to the knowledge gradient policy and its use with a wide range of belief models, including lookup table and parametric and for online and offline problems. Three sections develop ideas with increasing levels of sophistication:

  • Fundamentals explores fundamental topics, including adaptive learning, ranking and selection, the knowledge gradient, and bandit problems
  • Extensions and Applications features coverage of linear belief models, subset selection models, scalar function optimization, optimal bidding, and stopping problems
  • Advanced Topics explores complex methods including simulation optimization, active learning in mathematical programming, and optimal continuous measurements

Each chapter identifies a specific learning problem, presents the related, practical algorithms for implementation, and concludes with numerous exercises. A related website features additional applications and downloadable software, including MATLAB and the Optimal Learning Calculator, a spreadsheet-based package that provides an introduc­tion to learning and a variety of policies for learning.

Chapter

CONTENTS

pp.:  1 – 9

Preface

pp.:  9 – 17

Acknowledgments

pp.:  17 – 21

1 The Challenges of Learning

pp.:  21 – 23

2 Adaptive Learning

pp.:  23 – 53

3 The Economics of Information

pp.:  53 – 83

4 Ranking and Selection

pp.:  83 – 93

5 The Knowledge Gradient

pp.:  93 – 111

6 Bandit Problems

pp.:  111 – 161

7 Elements of a Learning Problem

pp.:  161 – 185

8 Linear Belief Models

pp.:  185 – 203

9 Subset Selection Problems

pp.:  203 – 225

10 Optimizing a Scalar Function

pp.:  225 – 241

11 Optimal Bidding

pp.:  241 – 253

12 Stopping Problems

pp.:  253 – 277

13 Active Learning in Statistics

pp.:  277 – 291

14 Simulation Optimization

pp.:  291 – 307

15 Learning in Mathematical Programming

pp.:  307 – 323

16 Optimizing Over Continuous Measurements

pp.:  323 – 347

17 Learning With a Physical State

pp.:  347 – 367

Index

pp.:  367 – 403

LastPages

pp.:  403 – 416

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