LEADER 05679nam 2200805Ia 450 001 9910810636103321 005 20200520144314.0 010 $a9786613622327 010 $a9781118309841 010 $a1118309847 010 $a9781280592492 010 $a1280592494 010 $a9781118304532 010 $a1118304535 010 $a9781118309827 010 $a1118309820 010 $a9781118309858 010 $a1118309855 035 $a(CKB)2670000000177364 035 $a(EBL)822054 035 $a(OCoLC)795912947 035 $a(SSID)ssj0000663826 035 $a(PQKBManifestationID)11447123 035 $a(PQKBTitleCode)TC0000663826 035 $a(PQKBWorkID)10614090 035 $a(PQKB)10454725 035 $a(MiAaPQ)EBC822054 035 $a(Au-PeEL)EBL822054 035 $a(CaPaEBR)ebr10560566 035 $a(CaONFJC)MIL362232 035 $a(Perlego)1011552 035 $a(EXLCZ)992670000000177364 100 $a20111116d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aOptimal learning /$fWarren B. Powell, Ilya O. Ryzhov 205 $a1st ed. 210 $aHoboken, NJ $cWiley$d2012 215 $a1 online resource (416 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 08$a9780470596692 311 08$a0470596694 320 $aIncludes bibliographical references and index. 327 $aOptimal Learning; CONTENTS; Preface; Acknowledgments; 1 The Challenges of Learning; 1.1 Learning the Best Path; 1.2 Areas of Application; 1.3 Major Problem Classes; 1.4 The Different Types of Learning; 1.5 Learning from Different Communities; 1.6 Information Collection Using Decision Trees; 1.6.1 A Basic Decision Tree; 1.6.2 Decision Tree for Offline Learning; 1.6.3 Decision Tree for Online Learning; 1.6.4 Discussion; 1.7 Website and Downloadable Software; 1.8 Goals of this Book; Problems; 2 Adaptive Learning; 2.1 The Frequentist View; 2.2 The Bayesian View 327 $a2.2.1 The Updating Equations for Independent Beliefs2.2.2 The Expected Value of Information; 2.2.3 Updating for Correlated Normal Priors; 2.2.4 Bayesian Updating with an Uninformative Prior; 2.3 Updating for Non-Gaussian Priors; 2.3.1 The Gamma-Exponential Model; 2.3.2 The Gamma-Poisson Model; 2.3.3 The Pareto-Uniform Model; 2.3.4 Models for Learning Probabilities*; 2.3.5 Learning an Unknown Variance*; 2.4 Monte Carlo Simulation; 2.5 Why Does It Work?*; 2.5.1 Derivation of ?; 2.5.2 Derivation of Bayesian Updating Equations for Independent Beliefs; 2.6 Bibliographic Notes; Problems 327 $a3 The Economics of Information3.1 An Elementary Information Problem; 3.2 The Marginal Value of Information; 3.3 An information Acquisition Problem; 3.4 Bibliographic Notes; Problems; 4 Ranking and Selection; 4.1 The Model; 4.2 Measurement Policies; 4.2.1 Deterministic Versus Sequential Policies; 4.2.2 Optimal Sequential Policies; 4.2.3 Heuristic Policies; 4.3 Evaluating Policies; 4.4 More Advanced Topics*; 4.4.1 An Alternative Representation of the Probability Space; 4.4.2 Equivalence of Using True Means and Sample Estimates; 4.5 Bibliographic Notes; Problems; 5 The Knowledge Gradient 327 $a5.1 The Knowledge Gradient for Independent Beliefs5.1.1 Computation; 5.1.2 Some Properties of the Knowledge Gradient; 5.1.3 The Four Distributions of Learning; 5.2 The Value of Information and the S-Curve Effect; 5.3 Knowledge Gradient for Correlated Beliefs; 5.4 Anticipatory Versus Experiential Learning; 5.5 The Knowledge Gradient for Some Non-Gaussian Distributions; 5.5.1 The Gamma-Exponential Model; 5.5.2 The Gamma-Poisson Model; 5.5.3 The Pareto-Uniform Model; 5.5.4 The Beta-Bernoulli Model; 5.5.5 Discussion; 5.6 Relatives of the Knowledge Gradient; 5.6.1 Expected Improvement 327 $a5.6.2 Linear Loss*5.7 The Problem of Priors; 5.8 Discussion; 5.9 Why Does It Work?*; 5.9.1 Derivation of the Knowledge Gradient Formula; 5.10 Bibliographic Notes; Problems; 6 Bandit Problems; 6.1 The Theory and Practice of Gittins Indices; 6.1.1 Gittins Indices in the Beta-Bernoulli Model; 6.1.2 Gittins Indices in the Normal-Normal Model; 6.1.3 Approximating Gittins Indices; 6.2 Variations of Bandit Problems; 6.3 Upper Confidence Bounding; 6.4 The Knowledge Gradient for Bandit Problems; 6.4.1 The Basic Idea; 6.4.2 Some Experimental Comparisons; 6.4.3 Non-Normal Models; 6.5 Bibliographic Notes 327 $aProblems 330 $aLearn the science of collecting information to make effective decisions Everyday decisions are made without the benefit of accurate information. Optimal Learning develops the needed principles for gathering information to make decisions, especially when collecting information is time-consuming and expensive. Designed for readers with an elementary background in probability and statistics, the book presents effective and practical policies illustrated in a wide range of applications, from energy, homeland security, and transportation to engineering, health, and business. T 410 0$aWiley series in probability and statistics. 606 $aMachine learning 606 $aArtificial intelligence 615 0$aMachine learning. 615 0$aArtificial intelligence. 676 $a006.3/1 686 $aMAT029000$2bisacsh 700 $aPowell$b Warren B.$f1955-$0882830 701 $aRyzhov$b Ilya Olegovich$f1985-$01696931 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910810636103321 996 $aOptimal learning$94077270 997 $aUNINA