Bandingkan metode

	Inferensi Variasional dengan Data Hilang	MCMC dengan Data Hilang
Bidang	Bayesian	Bayesian
Keluarga	Bayesian methods	Bayesian methods
Tahun asal	1994–2008	1987
Pencetus	Ghahramani & Jordan; Wainwright & Jordan (formal foundations)	Tanner & Wong (data augmentation); extended by Gelfand & Smith, Rubin
Tipe	Approximate Bayesian inference	Bayesian computational method
Sumber perintis	Ghahramani, Z. & Jordan, M. I. (1994). Supervised learning from incomplete data via an EM approach. In Cowan, J. D., Tesauro, G. & Alspector, J. (Eds.), Advances in Neural Information Processing Systems 6 (pp. 120–127). Morgan Kaufmann.	Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley. ISBN: 978-0471183860
Alias	VI with missing data, variational EM with missing data, VB missing data, mean-field VI for incomplete data	MCMC missing data, data augmentation MCMC, Bayesian multiple imputation, MCMC imputation
Terkait	4	6
Ringkasan	Variational inference with missing data is a scalable Bayesian approach that simultaneously approximates the posterior over latent variables and model parameters while imputing missing observations. Instead of integrating over all possible values of the missing entries exactly, it posits a tractable approximate distribution and optimises it to be as close as possible to the true joint posterior, yielding fast, principled inference even in high-dimensional incomplete datasets.	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.