Optimal Learning (Wiley Series in Probability and Statistics)

Optimal Learning (Wiley Series in Probability and Statistics)

<b>Learn the science of collecting information to make effective decisions</b> <p>Everyday decisions are made without the benefit of accurate information. <i>Optimal Learning</i> 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.</p> <p>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:</p> <ul> <li><b>Fundamentals</b> explores fundamental topics, including adaptive learning, ranking and selection, the knowledge gradient, and bandit problems</li> <li><b>Extensions and Applications</b> features coverage of linear belief models, subset selection models, scalar function optimization, optimal bidding, and stopping problems</li> <li><b>Advanced Topics</b> explores complex methods including simulation optimization, active learning in mathematical programming, and optimal continuous measurements</li> </ul> <p>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.</p>