Multinomial Thompson sampling for rating scales and prior considerations for calibrating uncertainty

DOI10.1007/s10260-023-00732-yMaRDI QIDQ6580646

Nina Deliu

Publication date: 29 July 2024

Published in: Statistical Methods and Applications (Search for Journal in Brave)

zbMATH Keywords

Dirichlet distributionmultinomial modelmulti-armed banditsincomplete learningThompson samplingrating scalesadaptive experiments

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

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• Multi-armed bandit models for the optimal design of clinical trials: benefits and challenges
• Asymptotically efficient adaptive allocation rules
• Bandit problems with infinitely many arms
• The class of \textsc{cub} models: statistical foundations, inferential issues and empirical evidence
• On the likelihood that one unkrown probability exeeds another in view of the evidence of two samples.
• On the Prior Sensitivity of Thompson Sampling
• Regression for Categorical Data
• Active Learning in Multi-armed Bandits
• A Tutorial on Thompson Sampling
• Near-Optimal Regret Bounds for Thompson Sampling
• On Incomplete Learning and Certainty-Equivalence Control
• Adaptive Treatment Assignment in Experiments for Policy Choice
• Bandit Algorithms
• A response‐adaptive randomization procedure for multi‐armed clinical trials with normally distributed outcomes
• Handbook of Statistical Methods for Randomized Controlled Trials
• Hierarchical marginal models with latent uncertainty
• Handbook of Multilevel Analysis
• Response-adaptive randomization in clinical trials: from myths to practical considerations
• Hidden Markov models for longitudinal rating data with dynamic response styles
• Randomization: the forgotten component of the randomized clinical trial

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