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Multi-Armed Bandit/Evidence
Method evidence record

Multi-Armed Bandit

The multi-armed bandit (MAB) is an adaptive experimental framework that allocates trials sequentially across competing arms to minimise cumulative regret while simultaneously learning which arm performs best. Formalised by Robbins in 1952 and given finite-time guarantees by Auer et al. (2002), it balances exploration of uncertain options against exploitation of currently known best options — outperforming classical A/B testing whenever early stopping or cost-sensitive allocation matters.

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Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Multi-Armed Bandit (UCB, Thompson Sampling)
Taxonomic method record · hypothesis-test / experimental-design
  • Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002). Finite-Time Analysis of the Multiarmed Bandit Problem. Machine Learning, 47(2–3), 235–256. · DOI 10.1023/A:1013689704352
  • Russo, D., Van Roy, B., Kazerouni, A., Osband, I., & Wen, Z. (2018). A Tutorial on Thompson Sampling. Foundations and Trends in Machine Learning, 11(1), 1–96. · DOI 10.1561/2200000070
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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Same method familyA/B Testmachine-suggested · Relational suggestion, not evidence.Same method familyAdaptive Clinical Trial Designmachine-suggested · Relational suggestion, not evidence.Same method familyRandomized Controlled Trialmachine-suggested · Relational suggestion, not evidence.Same method familySequential Designmachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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