Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Hamiltonian Monte Carlo na Data Zinazokosekana× | MCMC yenye Data Zilizokosekana× | |
|---|---|---|
| Nyanja | Mbinu za Bayes | Mbinu za Bayes |
| Familia | Bayesian methods | Bayesian methods |
| Mwaka wa asili≠ | 1996–2011 | 1987 |
| Mwanzilishi≠ | 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 |
| Aina≠ | Bayesian computational sampler | Bayesian computational method |
| Chanzo asilia≠ | 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 |
| Majina mbadala | 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 |
| Zinazohusiana | 6 | 6 |
| Muhtasari≠ | 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. |
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