Book contents
- Frontmatter
- Contents
- Preface
- Notation
- Part I Bandits, Probability and Concentration
- Part II Stochastic Bandits with Finitely Many Arms
- Part III Adversarial Bandits with Finitely Many Arms
- Part IV Lower Bounds for Bandits with Finitely Many Arms
- Part V Contextual and Linear Bandits
- Part VI Adversarial Linear Bandits
- Part VII Other Topics
- 30 Combinatorial Bandits
- 31 Non-stationary Bandits
- 32 Ranking
- 33 Pure Exploration
- 34 Foundations of Bayesian Learning
- 35 Bayesian Bandits
- 36 Thompson Sampling
- Part VIII Beyond Bandits
- Bibliography
- Index
36 - Thompson Sampling
from Part VII - Other Topics
Published online by Cambridge University Press: 04 July 2020
Book contents
- Frontmatter
- Contents
- Preface
- Notation
- Part I Bandits, Probability and Concentration
- Part II Stochastic Bandits with Finitely Many Arms
- Part III Adversarial Bandits with Finitely Many Arms
- Part IV Lower Bounds for Bandits with Finitely Many Arms
- Part V Contextual and Linear Bandits
- Part VI Adversarial Linear Bandits
- Part VII Other Topics
- 30 Combinatorial Bandits
- 31 Non-stationary Bandits
- 32 Ranking
- 33 Pure Exploration
- 34 Foundations of Bayesian Learning
- 35 Bayesian Bandits
- 36 Thompson Sampling
- Part VIII Beyond Bandits
- Bibliography
- Index
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- Information
- Bandit Algorithms , pp. 404 - 420Publisher: Cambridge University PressPrint publication year: 2020