优化学习 Optimal Learning

基本信息

Series:Wiley Series in Probability and Statistics

Format:Hardback 414 pages, Graphs: 50 B&W, 0 Color

Publisher:John Wiley & Sons Inc

Imprint:John Wiley & Sons Inc

ISBN:9780470596692

Published:27 Apr 2012

Weight:712g

Dimensions:241 x 164 (mm)

信息仅供参考，具体请以实物为准

书籍简介

学习收集信息以做出有效决策的科学

日常决策的制定离不开准确信息。《优化学习》提出了收集信息以做出决策所需的原则，尤其是在收集信息既耗时又费钱的情况下。本书专为具有概率和统计学基本背景的读者而设计，介绍了能源、国土安全和交通运输到工程、健康和商业等广泛应用中的有效实用政策。

本书涵盖了学习问题的基本维度，并介绍了一种测试和比较学习政策的简单方法。特别关注知识梯度策略及其在各种信念模型中的应用，包括查找表和参数以及在线和离线问题。三个部分以越来越复杂的程度发展思想：

基础知识探讨基本主题，包括自适应学习、排名和选择、知识梯度和强盗问题

扩展和应用涵盖线性信念模型、子集选择模型、标量函数优化、竞价和停止问题

高级主题探讨复杂方法，包括模拟优化、数学规划中的主动学习和连续测量

每章确定一个特定的学习问题，介绍相关的实用算法，并以大量练习结束。相关网站提供其他应用程序和可下载软件，包括 MATLAB 和学习计算器，这是一个基于电子表格的软件包，提供学习入门和各种学习策略。

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, andproblems

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.

作者简介

沃伦·鲍威尔博士是普林斯顿大学的运筹学和金融工程教授，也是 CASTLE 实验室的创始人和主任，该实验室是一个研究单位，与工业合作伙伴合作测试运筹学中的新想法。鲍威尔博士是 2004 年 INFORMS 研究员奖的获得者，著有《近似动态规划：解决维数灾难，第二版》（Wiley）。

伊利亚·奥·雷佐夫博士是马里兰大学罗伯特·史密斯商学院决策、运营和信息技术系的助理教授。他为将排名和选择领域与多臂老虎机以及学习与数学规划联系起来做出了根本性贡献。

WARREN B. POWELL, PhD, is Professor of Operations Research and Financial Engineering at Princeton University, where he is founder and Director of CASTLE Laboratory, a research unit that works with industrial partners to test new ideas found in operations research. The recipient of the 2004 INFORMS Fellow Award, Dr. Powell is the author of Approximate Dynamic Programming: Solving the Curses of Dimensionality, Second Edition (Wiley).

ILYA O. RYZHOV, PhD, is Assistant Professor in the Department of Decision, Operations, and Information Technologies at the Robert H. Smith School of Business at the University of Maryland. He has made fundamental contributions to bridge the fields of ranking and selection with multiarmed bandits and optimal learning with mathematical programming.

部分目录，仅供参考

Preface xv

Acknowledgments xix

1 The challenges of learning 1

1.1 Learning the best path 2

1.2 Areas of application 4

1.3 Major problem classes 12

1.4 The different types of learning 13

1.5 Learning from different communities 16

1.6 Information collection using decision trees 18

1.6.1 A basic decision tree 18

1.6.2 Decision tree for offline learning 20

1.6.3 Decision tree for online learning 21

1.6.4 Discussion 25

1.7 Website and downloadable software 26

1.8 Goals of this book 26

Problems 28

2 Adaptive learning 31

2.1 The frequentist view 32

2.2 The Bayesian view 33

2.2.1 The updating equations for independent beliefs 34

2.2.2 The expected value of information 36

2.2.3 Updating for correlated normal priors 38

2.2.4 Bayesian updating with an uninformative prior 41

2.3 Updating for non-Gaussian priors 42

2.3.1 The gamma-exponential model 43

2.3.2 The gamma-Poisson model 44