Optimal Learning / Wiley Series in Probability and Statistics (ePub)
(Sprache: Englisch)
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...
decisions
Everyday decisions are made without the benefit of accurate
information. Optimal Learning develops the needed principles
for gathering information to make decisions, especially...
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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.
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.
Autoren-Porträt von Warren B. Powell, Ilya O. Ryzhov
WARREN B. POWELL, PhD, is Professor of Operations Research andFinancial 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.
Bibliographische Angaben
- Autoren: Warren B. Powell , Ilya O. Ryzhov
- 2013, 1. Auflage, 414 Seiten, Englisch
- Verlag: John Wiley & Sons
- ISBN-10: 1118309847
- ISBN-13: 9781118309841
- Erscheinungsdatum: 09.07.2013
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
eBook Informationen
- Dateiformat: ePub
- Größe: 13 MB
- Mit Kopierschutz
Sprache:
Englisch
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