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- W4323572015 abstract "We consider Thompson sampling for linear bandit problems with finitely many independent arms, where rewards are sampled from normal distributions that are linearly dependent on unknown parameter vectors and with unknown variance. Specifically, with a Bayesian formulation we consider multivariate normal-gamma priors to represent environment uncertainty for all involved parameters. We show that our chosen sampling prior is a conjugate prior to the reward model and derive a Bayesian regret bound for Thompson sampling under the condition that the 5/2-moment of the variance distribution exist." @default.
- W4323572015 created "2023-03-09" @default.
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- W4323572015 date "2023-03-06" @default.
- W4323572015 modified "2023-09-23" @default.
- W4323572015 title "Thompson Sampling for Linear Bandit Problems with Normal-Gamma Priors" @default.
- W4323572015 doi "https://doi.org/10.48550/arxiv.2303.03348" @default.
- W4323572015 hasPublicationYear "2023" @default.
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