Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Адаптивне A/B тестування× | Експеримент з багатьма рукавами× | |
|---|---|---|
| Галузь | Планування експерименту | Планування експерименту |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 1952 (Robbins); applied to A/B testing from ~2010s onward | 1990s–2000s (clinical formalization); multi-arm concept implicit in ANOVA-era factorial designs |
| Автор методу≠ | Herbert Robbins (bandit framework); Thompson Sampling formalized by William R. Thompson | Developed within clinical trials methodology; formalized by Parmar, Royston and colleagues (UK MRC CTU, early 2000s) |
| Тип≠ | Adaptive experimental design | Experimental design |
| Основоположне джерело≠ | 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 ↗ | Royston, P., Parmar, M. K. B., & Qian, W. (2003). Novel designs for multi-arm clinical trials with survival outcomes with an application in ovarian cancer. Statistics in Medicine, 22(14), 2239–2256. DOI ↗ |
| Інші назви | adaptive AB test, bandit A/B test, multi-armed bandit testing, online adaptive experiment | multi-arm trial, multiple-arm experiment, multi-group experiment, many-arm design |
| Пов'язані≠ | 6 | 5 |
| Підсумок≠ | An Adaptive A/B test is an experimental design that dynamically reallocates traffic or participants toward better-performing variants during the experiment itself, rather than holding allocations fixed until the end. Drawing on multi-armed bandit algorithms such as Thompson Sampling or Upper Confidence Bound (UCB), it balances the exploration of uncertain variants with the exploitation of those already showing superior performance, typically yielding higher aggregate outcomes while still producing valid inferential conclusions. | A multi-arm experiment simultaneously compares three or more treatment or intervention conditions — each called an arm — against a shared control or against one another. By testing multiple alternatives in a single study, it yields more information per participant than running separate two-group experiments sequentially, while controlling the overall Type I error rate through pre-specified comparison strategies. |
| ScholarGateНабір даних ↗ |
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