Bandingkan metode

	Perhitungan Bayesian Aproksimatif dengan Data Hilang	MCMC dengan Data Hilang
Bidang	Bayesian	Bayesian
Keluarga	Bayesian methods	Bayesian methods
Tahun asal	2002 (ABC); 1987 (missing data theory)	1987
Pencetus	Beaumont, Zhang & Balding (ABC); Rubin (missing data framework)	Tanner & Wong (data augmentation); extended by Gelfand & Smith, Rubin
Tipe	likelihood-free Bayesian inference	Bayesian computational method
Sumber perintis	Beaumont, M. A., Zhang, W. & Balding, D. J. (2002). Approximate Bayesian computation in population genetics. Genetics, 162(4), 2025–2035.	Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley. ISBN: 978-0471183860
Alias	ABC with missing data, likelihood-free inference with missing data, simulation-based inference for incomplete data, ABC-MD	MCMC missing data, data augmentation MCMC, Bayesian multiple imputation, MCMC imputation
Terkait	6	6
Ringkasan	Approximate Bayesian Computation with missing data extends the likelihood-free ABC framework to settings where observations are incomplete or partially recorded. By simulating data under a posited model and accepting parameter draws whose simulated summary statistics are close to the observed ones, it bypasses the need to evaluate an intractable likelihood — even when some data values are absent.	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.