Bandingkan metode

	Inferensi Variasional dengan Data Hilang	Inferensi Bayesian dengan Data Hilang
Bidang	Bayesian	Bayesian
Keluarga	Bayesian methods	Bayesian methods
Tahun asal	1994–2008	1976–1987
Pencetus	Ghahramani & Jordan; Wainwright & Jordan (formal foundations)	Rubin, D. B. (missing-data mechanisms); Tanner & Wong (data augmentation)
Tipe	Approximate Bayesian inference	Bayesian probabilistic model
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-Interscience. ISBN: 978-0471183860
Alias	VI with missing data, variational EM with missing data, VB missing data, mean-field VI for incomplete data	Bayesian missing data analysis, Bayesian data augmentation, Bayesian imputation, missing data Bayesian model
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.	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.