Methoden vergleichen

	Dynamisches Hamiltonsches Monte-Carlo-Verfahren	Gibbs-Sampling
Fachgebiet	Bayes-Statistik	Bayes-Statistik
Familie	Bayesian methods	Bayesian methods
Entstehungsjahr	2014	1984
Urheber	Matthew D. Hoffman and Andrew Gelman	Stuart Geman & Donald Geman
Typ	adaptive MCMC sampler	MCMC sampling algorithm
Wegweisende Quelle	Hoffman, M. D. & Gelman, A. (2014). The No-U-Turn Sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research, 15(1), 1593–1623.	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.
Aliasnamen	Dynamic HMC, NUTS, No-U-Turn Sampler, adaptive HMC	Gibbs sampler, coordinate-wise MCMC, systematic scan Gibbs, blocked Gibbs sampling
Verwandt	5	5
Zusammenfassung	Dynamic Hamiltonian Monte Carlo — widely known as the No-U-Turn Sampler (NUTS) — is an adaptive extension of Hamiltonian Monte Carlo that automatically selects the number of leapfrog integration steps during each MCMC transition, removing the need to hand-tune the most sensitive tuning parameter of standard HMC. It is the default sampler in Stan and PyMC and is suitable for continuous, differentiable posterior distributions of moderate to high dimension.	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.