手法を比較

	欠損データを含むハミルトニアン・モンテカルロ法	欠損値を含むMCMC (MCMC with missing data)
分野	ベイズ	ベイズ
系統	Bayesian methods	Bayesian methods
提唱年	1996–2011	1987
提唱者	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
種類	Bayesian computational sampler	Bayesian computational method
原典	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
別名	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
関連	6	6
概要	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.