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Optimal learning / Warren B. Powell, Ilya O. Ryzhov, Robert H.

By: Powell, Warren BContributor(s): Ryzhov, Ilya OlegovichMaterial type: Text

TextSeries: Wiley series in probability and statisticsPublisher: Hoboken, New Jersey : Wiley, [2012]Description: xix, 384 pages : illustrations ; 24 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9780470596692 Subject(s): Machine learningLOC classification: Q325.5 | .P69 2012Summary: "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.

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Holdings
Item type	Current library	Call number	Status	Date due	Barcode	Item holds
BOOK	UNIMY PJ Library	519.542 P884o (Browse shelf (Opens below))	Available		100562

Total holds: 0

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Previous	519.5 ROC Data analysis with Microsoft Access 2010 : from simple queries to business intelligence /	519.50285 E45 Computational methods and statistics	519.50285554 ABB Understanding educational statistics using Microsoft Excel® and SPSS® /	519.542 P884o Optimal learning /	519.6 AME Computational elasticity theory of elasticity, finite and boundary element methods	519.7 BAZ Nonlinear programming theory and algorithms	530.02 VAN Computer solutions in physics : with applications in astrophysics, biophysics, differential equations, and engineering /	Next

Includes bibliographical references and index.

"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.

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