Porovnat metody

	Hamiltonian Monte Carlo s chybějícími daty	Bayesovská inference s chybějícími daty
Obor	Bayesovská statistika	Bayesovská statistika
Rodina	Bayesian methods	Bayesian methods
Rok vzniku	1996–2011	1976–1987
Tvůrce	Radford M. Neal (HMC, 1996/2011); missing-data treatment via Bayesian data augmentation (Tanner & Wong, 1987)	Rubin, D. B. (missing-data mechanisms); Tanner & Wong (data augmentation)
Typ	Bayesian computational sampler	Bayesian probabilistic model
Původní zdroj	Neal, R. M. (2011). MCMC using Hamiltonian dynamics. In S. Brooks, A. Gelman, G. Jones & X.-L. Meng (Eds.), Handbook of Markov Chain Monte Carlo (pp. 113-162). CRC Press. ISBN: 978-1420079418	Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley-Interscience. ISBN: 978-0471183860
Další názvy	HMC with missing data, HMC data augmentation, Bayesian HMC imputation, HMC with data augmentation	Bayesian missing data analysis, Bayesian data augmentation, Bayesian imputation, missing data Bayesian model
Příbuzné	6	6
Shrnutí	Hamiltonian Monte Carlo with missing data extends the gradient-based HMC sampler to handle incomplete observations by treating missing values as additional unknown parameters. The posterior over model parameters and missing values is sampled jointly in one efficient pass, exploiting gradient information to explore the high-dimensional joint space with far fewer rejected proposals than random-walk MCMC.	Bayesian inference with missing data treats unobserved values as unknown parameters and integrates them out of the posterior distribution. Rather than deleting or ad hoc imputing incomplete records, the method jointly models observed and missing data under an explicit missing-data mechanism, producing fully calibrated posterior uncertainty that honestly reflects what the data cannot tell us.