পদ্ধতির তুলনা করুন

	ডাইনামিক হ্যামিলটোনিয়ান মন্টি কার্লো	বেয়েশীয় রিগ্রেশন
ক্ষেত্র	বেইসীয়	বেইসীয়
পরিবার	Bayesian methods	Bayesian methods
উদ্ভবের বছর	2014	—
প্রবর্তক	Matthew D. Hoffman and Andrew Gelman	—
ধরন	adaptive MCMC sampler	Bayesian linear model
মৌলিক উৎস	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
অপর নাম	Dynamic HMC, NUTS, No-U-Turn Sampler, adaptive HMC	bayesian linear regression, probabilistic regression, bayesian regresyon
সম্পর্কিত	5	2
সারসংক্ষেপ	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.