Sammenlign metoder

	Flerniveau Bootstrap Simulering	Gibbs Sampling
Fagområde	Bayesiansk	Bayesiansk
Familie	Bayesian methods	Bayesian methods
Oprindelsesår	1979 (bootstrap); multilevel variants c.1990s	1984
Ophavsperson	Efron (1979); multilevel extensions developed through 1980s–2000s	Stuart Geman & Donald Geman
Type	resampling / simulation	MCMC sampling algorithm
Oprindelig kilde	Efron, B. (1979). Bootstrap methods: Another look at the jackknife. The Annals of Statistics, 7(1), 1–26.	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.
Aliasser	hierarchical bootstrap, cluster bootstrap, stratified bootstrap for multilevel data, multilevel resampling	Gibbs sampler, coordinate-wise MCMC, systematic scan Gibbs, blocked Gibbs sampling
Relaterede	6	5
Resumé	Multilevel bootstrap simulation is a resampling technique designed for clustered or hierarchically structured data. It preserves the nested data structure by resampling at each level independently — first drawing clusters (e.g., schools, hospitals), then drawing observations within each sampled cluster — so that bootstrap replicate datasets reflect the same multilevel organisation as the original data.	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.