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| Устойчиво осредняване на Байесови модели× | Йерархично Бейсианско заключение× | |
|---|---|---|
| Област | Бейсови методи | Бейсови методи |
| Семейство | Bayesian methods | Bayesian methods |
| Година на възникване≠ | 1999–2012 | 1972 (Lindley & Smith); consolidated 1995–2013 |
| Създател≠ | Hoeting, Madigan, Raftery, Volinsky (BMA); robustness extensions by Ley & Steel and others | Lindley & Smith; Gelman et al. |
| Тип≠ | Bayesian model selection and averaging | Bayesian multilevel model |
| Основополагащ източник≠ | Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382–401. link ↗ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 |
| Други названия | robust BMA, outlier-robust BMA, robust model averaging, heavy-tailed BMA | multilevel Bayesian modeling, Bayesian hierarchical model, nested Bayesian model, partial pooling model |
| Свързани | 6 | 6 |
| Резюме≠ | Robust Bayesian model averaging extends standard BMA by replacing sensitive conjugate priors with heavy-tailed or mixture priors (e.g., mixtures of g-priors), and optionally robust likelihoods, so that posterior model probabilities and averaged estimates remain stable when data contain outliers, influential observations, or when the prior on model parameters would otherwise dominate the results. | Hierarchical Bayesian inference is a probabilistic modeling framework that organises parameters into levels, placing priors on the group-level parameters and hyperpriors on the parameters governing those priors. It enables partial pooling of information across groups, balancing the extremes of treating each group as independent or merging them into a single estimate. |
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