This text presents optimal learning techniques with applications in energy, homeland security, health, sports, transportation science, biomedical research, biosurveillance, stochastic optimization, high technology, and complex resource allocation problems. The coverage utilizes a relatively new class of algorithmic strategies known as approximate dynamic programming, which merges dynamic programming (Markov decision processes), math programming (linear, nonlinear, and integer), simulation, and statistics. It features mathematical techniques that are applicable to a variety of situations, from identifying promising drug candidates to figuring out the best evacuation plan in the event of a natural disaster.
"This text presents optimal learning techniques with applications in energy, homeland security, health, sports, transportation science, biomedical research, biosurveillance, stochastic optimization, high technology, and complex resource allocation problems. The coverage utilizes a relatively new class of algorithmic strategies known as approximate dynamic programming, which merges dynamic programming (Markov decision processes), math programming (linear, nonlinear, and integer), simulation, and statistics. It features mathematical techniques that are applicable to a variety of situations, from identifying promising drug candidates to figuring out the best evacuation plan in the event of a natural disaster"--
Provided by publisher.
?Learn the science of collecting information to make effective decisionsEveryday 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 ex...(하략)
