Vertaile menetelmiä
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| Metropolis-Hastings menetelmä puuttuvalla datalla× | Metropolis-Hastings-algoritmi× | |
|---|---|---|
| Tieteenala | Bayesilainen tilastotiede | Bayesilainen tilastotiede |
| Menetelmäperhe | Bayesian methods | Bayesian methods |
| Syntyvuosi≠ | 1953 / 1987 | 1953 |
| Kehittäjä≠ | Metropolis et al. (1953); missing-data extension formalised by Tanner & Wong (1987) | Metropolis et al. (1953); generalised by Hastings (1970) |
| Tyyppi≠ | MCMC sampler with latent-variable augmentation | Markov chain Monte Carlo sampler |
| Alkuperäislähde≠ | Tanner, M. A. & Wong, W. H. (1987). The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association, 82(398), 528-540. DOI ↗ | Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21(6), 1087–1092. DOI ↗ |
| Rinnakkaisnimet≠ | MH with missing data, Metropolis-Hastings data augmentation, MCMC missing data imputation, MH data-augmentation sampler | MH algorithm, M-H algorithm, Metropolis algorithm, Metropolis-Hastings sampler |
| Liittyvät≠ | 6 | 5 |
| Tiivistelmä≠ | Metropolis-Hastings with missing data treats unobserved values as latent variables and samples them jointly with model parameters inside a single MCMC chain. By augmenting the target distribution to include both parameters and missing values, the algorithm yields properly calibrated posterior inference without discarding incomplete cases or requiring a separate imputation step. | The Metropolis-Hastings (MH) algorithm is a general-purpose Markov chain Monte Carlo (MCMC) method for drawing samples from any probability distribution whose density can be evaluated up to a normalising constant. Introduced by Metropolis, Rosenbluth, Rosenbluth, Teller, and Teller (1953) in computational physics and generalised by Hastings (1970) to asymmetric proposal distributions, it is the foundational algorithm from which nearly all subsequent MCMC samplers — Gibbs sampling, Hamiltonian Monte Carlo, slice sampling — are derived or can be viewed as special cases. |
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