Sammenlign metoder

	Multi-Armed Bandit (UCB, Thompson Sampling)	Sekventielt / Gruppesekventielt forsøgsdesign
Fagområde	Forsøgsdesign	Forsøgsdesign
Familie	Hypothesis test	Hypothesis test
Oprindelsesår	1952	1979
Ophavsperson	Robbins (1952); UCB1 by Auer et al. (2002); Thompson sampling by Thompson (1933)	O'Brien & Fleming; Pocock; Lan & DeMets
Type	Sequential decision / bandit algorithm	Adaptive stopping trial design
Oprindelig kilde	Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002). Finite-Time Analysis of the Multiarmed Bandit Problem. Machine Learning, 47(2–3), 235–256.	O'Brien, P.C. & Fleming, T.R. (1979). A Multiple Testing Procedure for Clinical Trials. Biometrics, 35(3), 549–556.
Aliasser	MAB, bandit algorithm, UCB1, Thompson sampling	group sequential design, adaptive stopping design, Ardışık Deneme Tasarımı (Sequential / Group Sequential)
Relaterede	4	3
Resumé	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.	Sequential and group sequential trial designs allow a study to be stopped early — or continued — based on interim analyses conducted as data accumulate. The core framework was formalised by O'Brien and Fleming in 1979 and extended by Lan and DeMets's alpha-spending approach, and it controls the overall Type I error rate across all planned looks by pre-specifying both efficacy and futility boundaries before enrolment begins.