Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Teste A/B (Experimento Controlado Online)× | Bandido Multiarmado (UCB, Amostragem de Thompson)× | |
|---|---|---|
| Área | Delineamento experimental | Delineamento experimental |
| Família | Hypothesis test | Hypothesis test |
| Ano de origem≠ | 1935 | 1952 |
| Autor original≠ | Ron Kohavi et al. (Microsoft); conceptual roots in R. A. Fisher's randomized experiments (1935) | Robbins (1952); UCB1 by Auer et al. (2002); Thompson sampling by Thompson (1933) |
| Tipo≠ | Parametric comparison (frequentist or Bayesian) | Sequential decision / bandit algorithm |
| Fonte seminal≠ | Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 9781108724265 | Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002). Finite-Time Analysis of the Multiarmed Bandit Problem. Machine Learning, 47(2–3), 235–256. DOI ↗ |
| Outros nomes≠ | split test, controlled experiment, two-variant test, A/B Testi (Online Kontrollü Deney) | MAB, bandit algorithm, UCB1, Thompson sampling |
| Relacionados | 4 | 4 |
| Resumo≠ | An A/B test is a randomized controlled experiment that simultaneously exposes two groups of users to a control variant (A) and a treatment variant (B) in order to determine whether a measured outcome differs significantly between them. The modern online controlled experiment framework was systematized by Ron Kohavi and colleagues at Microsoft in the early 2000s, building on R. A. Fisher's classical randomization principles from 1935. It is the dominant causal inference tool in web product development, digital marketing, and experimentation platforms. | 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. |
| ScholarGateConjunto de dados ↗ |
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