השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| רב-זרועי (UCB, Thompson Sampling)× | תכנון ניסויים עוקבים / עוקבים מקובצים× | |
|---|---|---|
| תחום | תכנון ניסויים | תכנון ניסויים |
| משפחה | Hypothesis test | Hypothesis test |
| שנת המקור≠ | 1952 | 1979 |
| הוגה השיטה≠ | Robbins (1952); UCB1 by Auer et al. (2002); Thompson sampling by Thompson (1933) | O'Brien & Fleming; Pocock; Lan & DeMets |
| סוג≠ | Sequential decision / bandit algorithm | Adaptive stopping trial design |
| מקור מכונן≠ | Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002). Finite-Time Analysis of the Multiarmed Bandit Problem. Machine Learning, 47(2–3), 235–256. DOI ↗ | O'Brien, P.C. & Fleming, T.R. (1979). A Multiple Testing Procedure for Clinical Trials. Biometrics, 35(3), 549–556. DOI ↗ |
| כינויים≠ | MAB, bandit algorithm, UCB1, Thompson sampling | group sequential design, adaptive stopping design, Ardışık Deneme Tasarımı (Sequential / Group Sequential) |
| קשורות≠ | 4 | 3 |
| תקציר≠ | 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. |
| ScholarGateמערך נתונים ↗ |
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