Salīdzināt metodes

	Dinamiskā Hamiltona Monte Karlo metode	Gibbs Sampling
Nozare	Bajesa metodes	Bajesa metodes
Saime	Bayesian methods	Bayesian methods
Izcelsmes gads	2014	1984
Autors	Matthew D. Hoffman and Andrew Gelman	Stuart Geman & Donald Geman
Tips	adaptive MCMC sampler	MCMC sampling algorithm
Pirmavots	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.
Citi nosaukumi	Dynamic HMC, NUTS, No-U-Turn Sampler, adaptive HMC	Gibbs sampler, coordinate-wise MCMC, systematic scan Gibbs, blocked Gibbs sampling
Saistītās	5	5
Kopsavilkums	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.