Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Гамильтоновский Монте-Карло с пропущенными данными× | MCMC с пропущенными данными× | |
|---|---|---|
| Область | Байесовские методы | Байесовские методы |
| Семейство | Bayesian methods | Bayesian methods |
| Год появления≠ | 1996–2011 | 1987 |
| Автор метода≠ | Radford M. Neal (HMC, 1996/2011); missing-data treatment via Bayesian data augmentation (Tanner & Wong, 1987) | Tanner & Wong (data augmentation); extended by Gelfand & Smith, Rubin |
| Тип≠ | Bayesian computational sampler | Bayesian computational method |
| Основополагающий источник≠ | Neal, R. M. (2011). MCMC using Hamiltonian dynamics. In S. Brooks, A. Gelman, G. Jones & X.-L. Meng (Eds.), Handbook of Markov Chain Monte Carlo (pp. 113-162). CRC Press. ISBN: 978-1420079418 | Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley. ISBN: 978-0471183860 |
| Другие названия | HMC with missing data, HMC data augmentation, Bayesian HMC imputation, HMC with data augmentation | MCMC missing data, data augmentation MCMC, Bayesian multiple imputation, MCMC imputation |
| Связанные | 6 | 6 |
| Сводка≠ | Hamiltonian Monte Carlo with missing data extends the gradient-based HMC sampler to handle incomplete observations by treating missing values as additional unknown parameters. The posterior over model parameters and missing values is sampled jointly in one efficient pass, exploiting gradient information to explore the high-dimensional joint space with far fewer rejected proposals than random-walk MCMC. | MCMC with missing data is a Bayesian computational strategy that treats unobserved values as additional unknown parameters. By alternating between sampling the missing values from their predictive distribution and sampling the model parameters from their posterior, the algorithm produces a valid joint posterior that fully accounts for uncertainty introduced by the missingness. |
| ScholarGateНабор данных ↗ |
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