05679nam 2200805Ia 450 991081063610332120200520144314.097866136223279781118309841111830984797812805924921280592494978111830453211183045359781118309827111830982097811183098581118309855(CKB)2670000000177364(EBL)822054(OCoLC)795912947(SSID)ssj0000663826(PQKBManifestationID)11447123(PQKBTitleCode)TC0000663826(PQKBWorkID)10614090(PQKB)10454725(MiAaPQ)EBC822054(Au-PeEL)EBL822054(CaPaEBR)ebr10560566(CaONFJC)MIL362232(Perlego)1011552(EXLCZ)99267000000017736420111116d2012 uy 0engur|n|---|||||txtccrOptimal learning /Warren B. Powell, Ilya O. Ryzhov1st ed.Hoboken, NJ Wiley20121 online resource (416 p.)Wiley series in probability and statisticsDescription based upon print version of record.9780470596692 0470596694 Includes bibliographical references and index.Optimal 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 View2.2.1 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 σ; 2.5.2 Derivation of Bayesian Updating Equations for Independent Beliefs; 2.6 Bibliographic Notes; Problems3 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 Gradient5.1 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 Improvement5.6.2 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 NotesProblemsLearn 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. TWiley series in probability and statistics.Machine learningArtificial intelligenceMachine learning.Artificial intelligence.006.3/1MAT029000bisacshPowell Warren B.1955-882830Ryzhov Ilya Olegovich1985-1696931MiAaPQMiAaPQMiAaPQBOOK9910810636103321Optimal learning4077270UNINA