Salīdzināt metodes

	Telpiskā Gibssas izlase	Gibbs Sampling
Nozare	Bajesa metodes	Bajesa metodes
Saime	Bayesian methods	Bayesian methods
Izcelsmes gads	1984	1984
Autors	Stuart Geman and Donald Geman	Stuart Geman & Donald Geman
Tips	MCMC sampling algorithm for spatial models	MCMC sampling algorithm
Pirmavots	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.	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.
Citi nosaukumi	Gibbs sampler for spatial models, MRF Gibbs sampling, spatial MCMC via Gibbs, conditional field simulation	Gibbs sampler, coordinate-wise MCMC, systematic scan Gibbs, blocked Gibbs sampling
Saistītās	4	5
Kopsavilkums	Spatial Gibbs sampling applies the Gibbs sampler — a coordinate-wise Markov chain Monte Carlo algorithm — to models where observations are arranged in space and nearby locations are statistically dependent. By exploiting the conditional independence implied by a spatial neighbourhood structure, each site is updated one at a time given its neighbours, making posterior inference tractable for Markov random fields, Gaussian random fields, and hierarchical geostatistical models.	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.