Bandingkan kaedah

	Hamiltonian Monte Carlo dengan Data Hilang	MCMC dengan Data Hilang
Bidang	Bayesian	Bayesian
Keluarga	Bayesian methods	Bayesian methods
Tahun asal	1996–2011	1987
Pengasas	Radford M. Neal (HMC, 1996/2011); missing-data treatment via Bayesian data augmentation (Tanner & Wong, 1987)	Tanner & Wong (data augmentation); extended by Gelfand & Smith, Rubin
Jenis	Bayesian computational sampler	Bayesian computational method
Sumber perintis	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. ISBN: 978-0471183860
Alias	HMC with missing data, HMC data augmentation, Bayesian HMC imputation, HMC with data augmentation	MCMC missing data, data augmentation MCMC, Bayesian multiple imputation, MCMC imputation
Berkaitan	6	6
Ringkasan	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.	MCMC with missing data is a Bayesian computational strategy that treats unobserved values as additional unknown parameters. By alternating between sampling the missing values from their predictive distribution and sampling the model parameters from their posterior, the algorithm produces a valid joint posterior that fully accounts for uncertainty introduced by the missingness.