Optimal Learning
Book Details
Format
Hardback or Cased Book
Book Series
Wiley Series in Probability and Statistics
ISBN-10
0470596694
ISBN-13
9780470596692
Publisher
John Wiley & Sons Inc
Imprint
John Wiley & Sons Inc
Country of Manufacture
US
Country of Publication
GB
Publication Date
Apr 27th, 2012
Print length
414 Pages
Weight
712 grams
Dimensions
24.10 x 16.40 x 2.70 cms
Product Classification:
Applied mathematicsMachine learning
Ksh 19,050.00
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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.
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 problemsExtensions and Applications features coverage of linear belief models, subset selection models, scalar function optimization, optimal bidding, and stopping problemsAdvanced 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.
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