Comparer des méthodes

	Monte Carlo Hamiltonien Dynamique	Régression bayésienne
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	2014	—
Auteur d'origine	Matthew D. Hoffman and Andrew Gelman	—
Type	adaptive MCMC sampler	Bayesian linear model
Source fondatrice	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.	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955
Alias	Dynamic HMC, NUTS, No-U-Turn Sampler, adaptive HMC	bayesian linear regression, probabilistic regression, bayesian regresyon
Apparentées	5	2
Résumé	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.	Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.