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Optimal learning /
"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 ne...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
Wiley,
©2012.
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| Colección: | Wiley series in probability and statistics.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Powell, Warren B., |d 1955- |1 https://id.oclc.org/worldcat/entity/E39PCjBXJFxYXPVf84dQPcpfpX | |
| 245 | 1 | 0 | |a Optimal learning / |c Warren B. Powell, Operations Research and Financial Engineering, Princeton University, Ilya O. Ryzhov, Robert H. Smith School of Business, University of Maryland. |
| 260 | |a Hoboken, New Jersey : |b Wiley, |c ©2012. | ||
| 300 | |a 1 online resource (xix, 384 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 347 | |a text file | ||
| 490 | 1 | |a Wiley series in probability and statistics | |
| 505 | 0 | |6 880-01 |a Front Matter -- The Challenges of Learning -- Adaptive Learning -- The Economics of Information -- Ranking and Selection -- The Knowledge Gradient -- Bandit Problems -- Elements of a Learning Problem -- Linear Belief Models -- Subset Selection Problems -- Optimizing a Scalar Function -- Optimal Bidding -- Stopping Problems -- Active Learning in Statistics -- Simulation Optimization -- Learning in Mathematical Programming -- Optimizing Over Continuous Measurements -- Learning with a Physical State -- Bibliography -- Index -- Wiley Series in Probability and Statistics. | |
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a "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 | ||
| 588 | 0 | |a Print version record. | |
| 546 | |a English. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a Machine learning |2 fast | |
| 700 | 1 | |a Ryzhov, Ilya Olegovich, |d 1985- |1 https://id.oclc.org/worldcat/entity/E39PCjqydxbGX4TmBg3PmvbxtC | |
| 758 | |i has work: |a Optimal learning (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGCM47GDVB8YvQWKRG6RJC |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Powell, Warren B., 1955- |t Optimal learning. |d Hoboken, New Jersey : Wiley, ©2012 |z 9780470596692 |w (DLC) 2011047629 |w (OCoLC)760978407 |
| 830 | 0 | |a Wiley series in probability and statistics. | |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=822054 |z Texto completo |
| 880 | 0 | 0 | |6 505-01/(S |g Machine generated contents note: |g 1. |t Challenges of Learning -- |g 1.1. |t Learning the Best Path -- |g 1.2. |t Areas of Application -- |g 1.3. |t Major Problem Classes -- |g 1.4. |t Different Types of Learning -- |g 1.5. |t Learning from Different Communities -- |g 1.6. |t Information Collection Using Decision Trees -- |g 1.6.1. |t Basic Decision Tree -- |g 1.6.2. |t Decision Tree for Offline Learning -- |g 1.6.3. |t Decision Tree for Online Learning -- |g 1.6.4. |t Discussion -- |g 1.7. |t Website and Downloadable Software -- |g 1.8. |t Goals of this Book -- |t Problems -- |g 2. |t Adaptive Learning -- |g 2.1. |t Frequentist View -- |g 2.2. |t Bayesian View -- |g 2.2.1. |t Updating Equations for Independent Beliefs -- |g 2.2.2. |t Expected Value of Information -- |g 2.2.3. |t Updating for Correlated Normal Priors -- |g 2.2.4. |t Bayesian Updating with an Uninformative Prior -- |g 2.3. |t Updating for Non-Gaussian Priors -- |g 2.3.1. |t Gamma-Exponential Model -- |g 2.3.2. |t Gamma-Poisson Model -- |g 2.3.3. |t Pareto-Uniform Model -- |g 2.3.4. |t Models for Learning Probabilities* -- |g 2.3.5. |t Learning an Unknown Variance* -- |g 2.4. |t Monte Carlo Simulation -- |g 2.5. |t Why Does It Work* -- |g 2.5.1. |t Derivation of σ -- |g 2.5.2. |t Derivation of Bayesian Updating Equations for Independent Beliefs -- |g 2.6. |t Bibliographic Notes -- |t Problems -- |g 3. |t Economics of Information -- |g 3.1. |t Elementary Information Problem -- |g 3.2. |t Marginal Value of Information -- |g 3.3. |t information Acquisition Problem -- |g 3.4. |t Bibliographic Notes -- |t Problems -- |g 4. |t Ranking and Selection -- |g 4.1. |t Model -- |g 4.2. |t Measurement Policies -- |g 4.2.1. |t Deterministic Versus Sequential Policies -- |g 4.2.2. |t Optimal Sequential Policies -- |g 4.2.3. |t Heuristic Policies -- |g 4.3. |t Evaluating Policies -- |g 4.4. |t More Advanced Topics* -- |g 4.4.1. |t Alternative Representation of the Probability Space -- |g 4.4.2. |t Equivalence of Using True Means and Sample Estimates -- |g 4.5. |t Bibliographic Notes -- |t Problems -- |g 5. |t Knowledge Gradient -- |g 5.1. |t Knowledge Gradient for Independent Beliefs -- |g 5.1.1. |t Computation -- |g 5.1.2. |t Some Properties of the Knowledge Gradient -- |g 5.1.3. |t Four Distributions of Learning -- |g 5.2. |t Value of Information and the S-Curve Effect -- |g 5.3. |t Knowledge Gradient for Correlated Beliefs -- |g 5.4. |t Anticipatory Versus Experiential Learning -- |g 5.5. |t Knowledge Gradient for Some Non-Gaussian Distributions -- |g 5.5.1. |t Gamma-Exponential Model -- |g 5.5.2. |t Gamma-Poisson Model -- |g 5.5.3. |t Pareto-Uniform Model -- |g 5.5.4. |t Beta-Bernoulli Model -- |g 5.5.5. |t Discussion -- |g 5.6. |t Relatives of the Knowledge Gradient -- |g 5.6.1. |t Expected Improvement -- |g 5.6.2. |t Linear Loss* -- |g 5.7. |t Problem of Priors -- |g 5.8. |t Discussion -- |g 5.9. |t Why Does It Work* -- |g 5.9.1. |t Derivation of the Knowledge Gradient Formula -- |g 5.10. |t Bibliographic Notes -- |t Problems -- |g 6. |t Bandit Problems -- |g 6.1. |t Theory and Practice of Gittins Indices -- |g 6.1.1. |t Gittins Indices in the Beta-Bernoulli Model -- |g 6.1.2. |t Gittins Indices in the Normal-Normal Model -- |g 6.1.3. |t Approximating Gittins Indices -- |g 6.2. |t Variations of Bandit Problems -- |g 6.3. |t Upper Confidence Bounding -- |g 6.4. |t Knowledge Gradient for Bandit Problems -- |g 6.4.1. |t Basic Idea -- |g 6.4.2. |t Some Experimental Comparisons -- |g 6.4.3. |t Non-Normal Models -- |g 6.5. |t Bibliographic Notes -- |t Problems -- |g 7. |t Elements of a Learning Problem -- |g 7.1. |t States of our System -- |g 7.2. |t Types of Decisions -- |g 7.3. |t Exogenous Information -- |g 7.4. |t Transition Functions -- |g 7.5. |t Objective Functions -- |g 7.5.1. |t Designing Versus Controlling -- |g 7.5.2. |t Measurement Costs -- |g 7.5.3. |t Objectives -- |g 7.6. |t Evaluating Policies -- |g 7.7. |t Discussion -- |g 7.8. |t Bibliographic Notes -- |t Problems -- |g 8. |t Linear Belief Models -- |g 8.1. |t Applications -- |g 8.1.1. |t Maximizing Ad Clicks -- |g 8.1.2. |t Dynamic Pricing -- |g 8.1.3. |t Housing Loans -- |g 8.1.4. |t Optimizing Dose Response -- |g 8.2. |t Brief Review of Linear Regression -- |g 8.2.1. |t Normal Equations -- |g 8.2.2. |t Recursive Least Squares -- |g 8.2.3. |t Bayesian Interpretation -- |g 8.2.4. |t Generating a Prior -- |g 8.3. |t Knowledge Gradient for a Linear Model -- |g 8.4. |t Application to Drug Discovery -- |g 8.5. |t Application to Dynamic Pricing -- |g 8.6. |t Bibliographic Notes -- |t Problems -- |g 9. |t Subset Selection Problems -- |g 9.1. |t Applications -- |g 9.2. |t Choosing a Subset Using Ranking and Selection -- |g 9.2.1. |t Setting Prior Means and Variances -- |g 9.2.2. |t Two Strategies for Setting Prior Covariances -- |g 9.3. |t Larger Sets -- |g 9.3.1. |t Using Simulation to Reduce the Problem Size -- |g 9.3.2. |t Computational Issues -- |g 9.3.3. |t Experiments -- |g 9.4. |t Very Large Sets -- |g 9.5. |t Bibliographic Notes -- |t Problems -- |g 10. |t Optimizing a Scalar Function -- |g 10.1. |t Deterministic Measurements -- |g 10.2. |t Stochastic Measurements -- |g 10.2.1. |t Model -- |g 10.2.2. |t Finding the Posterior Distribution -- |g 10.2.3. |t Choosing the Measurement -- |g 10.2.4. |t Discussion -- |g 10.3. |t Bibliographic Notes -- |t Problems -- |g 11. |t Optimal Bidding -- |g 11.1. |t Modeling Customer Demand -- |g 11.1.1. |t Some Valuation Models -- |g 11.1.2. |t Logit Model -- |g 11.2. |t Bayesian Modeling for Dynamic Pricing -- |g 11.2.1. |t Conjugate Prior for Choosing Between Two Demand Curves -- |g 11.2.2. |t Moment Matching for Nonconjugate Problems -- |g 11.2.3. |t Approximation for the Logit Model -- |g 11.3. |t Bidding Strategies -- |g 11.3.1. |t Idea From Multi-Armed Bandits -- |g 11.3.2. |t Bayes-Greedy Bidding -- |g 11.3.3. |t Numerical Illustrations -- |g 11.4. |t Why Does It Work* -- |g 11.4.1. |t Moment Matching for Pareto Prior -- |g 11.4.2. |t Approximating the Logistic Expectation -- |g 11.5. |t Bibliographic Notes -- |t Problems -- |g 12. |t Stopping Problems -- |g 12.1. |t Sequential Probability Ratio Test -- |g 12.2. |t Secretary Problem -- |g 12.2.1. |t Setup -- |g 12.2.2. |t Solution -- |g 12.3. |t Bibliographic Notes -- |t Problems -- |g 13. |t Active Learning in Statistics -- |g 13.1. |t Deterministic Policies -- |g 13.2. |t Sequential Policies for Classification -- |g 13.2.1. |t Uncertainty Sampling -- |g 13.2.2. |t Query by Committee -- |g 13.2.3. |t Expected Error Reduction -- |g 13.3. |t Variance-Minimizing Policy -- |g 13.4. |t Mixtures of Gaussians -- |g 13.4.1. |t Estimating Parameters -- |g 13.4.2. |t Active Learning -- |g 13.5. |t Bibliographic Notes -- |g 14. |t Simulation Optimization -- |g 14.1. |t Indifference Zone Selection -- |g 14.1.1. |t Batch Procedures -- |g 14.1.2. |t Sequential Procedures -- |g 14.1.3. |t 0-1 Procedure: Connection to Linear Loss -- |g 14.2. |t Optimal Computing Budget Allocation -- |g 14.2.1. |t Indifference-Zone Version -- |g 14.2.2. |t Linear Loss Version -- |g 14.2.3. |t When Does It Work-- |g 14.3. |t Model-Based Simulated Annealing -- |g 14.4. |t Other Areas of Simulation Optimization -- |g 14.5. |t Bibliographic Notes -- |g 15. |t Learning in Mathematical Programming -- |g 15.1. |t Applications -- |g 15.1.1. |t Piloting a Hot Air Balloon -- |g 15.1.2. |t Optimizing a Portfolio -- |g 15.1.3. |t Network Problems -- |g 15.1.4. |t Discussion -- |g 15.2. |t Learning on Graphs -- |g 15.3. |t Alternative Edge Selection Policies -- |g 15.4. |t Learning Costs for Linear Programs* -- |g 15.5. |t Bibliographic Notes -- |g 16. |t Optimizing Over Continuous Measurements -- |g 16.1. |t Belief Model -- |g 16.1.1. |t Updating Equations -- |g 16.1.2. |t Parameter Estimation -- |g 16.2. |t Sequential Kriging Optimization -- |g 16.3. |t Knowledge Gradient for Continuous Parameters* -- |g 16.3.1. |t Maximizing the Knowledge Gradient -- |g 16.3.2. |t Approximating the Knowledge Gradient -- |g 16.3.3. |t Gradient of the Knowledge Gradient -- |g 16.3.4. |t Maximizing the Knowledge Gradient -- |g 16.3.5. |t KGCP Policy -- |g 16.4. |t Efficient Global Optimization -- |g 16.5. |t Experiments -- |g 16.6. |t Extension to Higher-Dimensional Problems -- |g 16.7. |t Bibliographic Notes -- |g 17. |t Learning With a Physical State -- |g 17.1. |t Introduction to Dynamic Programming -- |g 17.1.1. |t Approximate Dynamic Programming -- |g 17.1.2. |t Exploration vs. Exploitation Problem -- |g 17.1.3. |t Discussion -- |g 17.2. |t Some Heuristic Learning Policies -- |g 17.3. |t Local Bandit Approximation -- |g 17.4. |t Knowledge Gradient in Dynamic Programming -- |g 17.4.1. |t Generalized Learning Using Basis Functions -- |g 17.4.2. |t Knowledge Gradient -- |g 17.4.3. |t Experiments -- |g 17.5. |t Expected Improvement Policy -- |g 17.6. |t Bibliographic Notes. |
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