Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Робастное байесовское усреднение моделей× | Метод Монте-Карло по цепям Маркова (MCMC)× | |
|---|---|---|
| Область | Байесовские методы | Байесовские методы |
| Семейство | Bayesian methods | Bayesian methods |
| Год появления≠ | 1999–2012 | — |
| Автор метода≠ | Hoeting, Madigan, Raftery, Volinsky (BMA); robustness extensions by Ley & Steel and others | — |
| Тип≠ | Bayesian model selection and averaging | Posterior sampling algorithm |
| Основополагающий источник≠ | 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 |
| Другие названия≠ | robust BMA, outlier-robust BMA, robust model averaging, heavy-tailed BMA | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| Связанные≠ | 6 | 3 |
| Сводка≠ | 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. | Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model. |
| ScholarGateНабор данных ↗ |
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