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The Elements of Joint Learning and Optimization in Operations Management

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Cover of 'The Elements of Joint Learning and Optimization in Operations Management'

Table of Contents

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    Book Overview
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    Chapter 1 The Stochastic Multi-Armed Bandit Problem
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    Chapter 2 Reinforcement Learning
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    Chapter 3 Optimal Learning and Optimal Design
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    Chapter 4 Dynamic Pricing with Demand Learning: Emerging Topics and State of the Art
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    Chapter 5 Learning and Pricing with Inventory Constraints
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    Chapter 6 Dynamic Pricing and Demand Learning in Nonstationary Environments
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    Chapter 7 Pricing with High-Dimensional Data
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    Chapter 8 Nonparametric Estimation of Choice Models
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    Chapter 9 The MNL-Bandit Problem
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    Chapter 10 Dynamic Assortment Optimization: Beyond MNL Model
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    Chapter 11 Inventory Control with Censored Demand
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    Chapter 12 Joint Pricing and Inventory Control with Demand Learning
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    Chapter 13 Optimization in the Small-Data, Large-Scale Regime
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    Chapter 14 Bandit Procedures for Designing Patient-Centric Clinical Trials
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    Chapter 15 Dynamic Treatment Regimes for Optimizing Healthcare
Attention for Chapter 8: Nonparametric Estimation of Choice Models
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Chapter title
Nonparametric Estimation of Choice Models
Chapter number 8
Book title
The Elements of Joint Learning and Optimization in Operations Management
Published by
Springer, Cham, January 2022
DOI 10.1007/978-3-031-01926-5_8
Book ISBNs
978-3-03-101925-8, 978-3-03-101926-5
Authors

Jagabathula, Srikanth, Venkataraman, Ashwin

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