Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Slice Sampling× | Gibbs sampling× | |
|---|---|---|
| Ämnesområde | Bayesiansk statistik | Bayesiansk statistik |
| Familj | Bayesian methods | Bayesian methods |
| Ursprungsår≠ | 2003 | 1984 |
| Upphovsperson≠ | Radford M. Neal | Stuart Geman & Donald Geman |
| Typ | MCMC sampling algorithm | MCMC sampling algorithm |
| Ursprungskälla≠ | Neal, R. M. (2003). Slice sampling (with discussion). Annals of Statistics, 31(3), 705–767. DOI ↗ | Geman, S. & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6), 721-741. DOI ↗ |
| Alias | slice sampler, Neal slice sampler, uniform slice sampling, auxiliary variable slice sampler | Gibbs sampler, coordinate-wise MCMC, systematic scan Gibbs, blocked Gibbs sampling |
| Närliggande≠ | 4 | 5 |
| Sammanfattning≠ | Slice sampling is a Markov chain Monte Carlo (MCMC) algorithm introduced by Radford M. Neal in his 2003 Annals of Statistics paper. It generates samples from a target distribution by drawing uniformly from the region under the density curve — called the 'slice' — without requiring the user to specify a step-size or proposal distribution, making it self-tuning and broadly applicable for Bayesian posterior inference. | Gibbs sampling is a Markov chain Monte Carlo algorithm that approximates a high-dimensional posterior distribution by repeatedly drawing each parameter from its full conditional distribution given all other parameters and the data. Because each draw is exact from a conditional — not a proposal that may be rejected — the sampler is efficient when those conditionals are available in closed form. |
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