Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Daudzroku bandit (UCB, Tompsona izlase)× | Adaptīvā klīnisko pētījumu dizains× | |
|---|---|---|
| Nozare | Eksperimentu plānošana | Eksperimentu plānošana |
| Saime | Hypothesis test | Hypothesis test |
| Izcelsmes gads≠ | 1952 | 1994 |
| Autors≠ | Robbins (1952); UCB1 by Auer et al. (2002); Thompson sampling by Thompson (1933) | Bauer & Köhne |
| Tips≠ | Sequential decision / bandit algorithm | Adaptive hypothesis test with interim analyses |
| Pirmavots≠ | Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002). Finite-Time Analysis of the Multiarmed Bandit Problem. Machine Learning, 47(2–3), 235–256. DOI ↗ | Bauer, P. & Köhne, K. (1994). Evaluation of Experiments with Adaptive Interim Analyses. Biometrics, 50(4), 1029–1041. DOI ↗ |
| Citi nosaukumi≠ | MAB, bandit algorithm, UCB1, Thompson sampling | adaptive design, group sequential design, sample size re-estimation, platform trial |
| Saistītās≠ | 4 | 3 |
| Kopsavilkums≠ | 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. | Adaptive clinical trial design is a flexible experimental framework, formalised by Bauer and Köhne in 1994, in which pre-specified rules allow the trial to be modified mid-course — adjusting sample size, treatment arms, or randomisation ratios — based on accumulating interim data while rigorously controlling the Type I error rate. |
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