Optimal Learning (eBook)

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 introduc-tion to learning and a variety of
policies for learning.